Assessing the Impact and Root Causes of Medical Errors in a Multispeciality Hospital

Assessing the Impact and Root Causes of Medical Errors in a Multispeciality Hospital | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing the Impact and Root Causes of Medical Errors in a Multispeciality Hospital Immanuel Devakumar, Poomagal Adhinarayanan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6554912/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Medical errors remain one of the leading causes of preventable harm in healthcare systems, particularly in complex Multispeciality hospital settings. Despite the implementation of structured safety frameworks and guidelines such as the National Accreditation Board for Hospitals & Healthcare Providers (NABH) accreditation, there are persistent challenges in identifying and addressing the root causes of medical errors. This study aims to systematically evaluate the types, frequencies, and underlying causes of medical errors in a Multispeciality hospital environment and assess the impact of quality improvement initiatives in mitigating these errors. Methods This study employed a retrospective descriptive-analytical design at a NABH-accredited Multispeciality hospital, analysing inpatient data collected over a six-month period (July to December 2024). A Purposive sampling method was applied to select and review incident reports, inpatient records, and quality audit logs. Various analytical tools including chi-square tests, Pearson's correlation, Root Cause Analysis (RCA), Failure Mode and Effects Analysis (FMEA), and Corrective and Preventive Action (CAPA) were used for data analysis. Statistical analyses were performed using SPSS version 25. Results A total of 132 medical errors were identified, with documentation errors (28.7%), diagnostic delays (24.2%), and communication failures (19.6%) being the most prevalent. Root cause analysis (RCA) and failure mode effects analysis (FMEA) identified systemic issues such as inconsistent documentation practices, poor interdepartmental communication, and informal handoff protocols as key contributors to these errors. A chi-square test revealed a significant association between low surgical safety adherence and an increased frequency of transition handoff errors (χ² = 45.92, df = 1, p < 0.01). Statistical analyses revealed significant relationships between error types and hospital departments (χ² (16) = 57.23, p < 0.01), with surgical errors most common in surgical units and diagnostic errors prevalent in radiology. A moderate positive correlation (r = 0.52, p < 0.05) was found between the frequency of medical errors and the length of hospital stay, highlighting the broader impact of errors on patient outcomes and healthcare costs. Following the implementation of corrective actions and quality improvement measures, including CAPA protocols, Lean Six Sigma, and enhanced checklist usage, a 30% reduction in error rates was observed by December 2024. Conclusions This study demonstrates that integrating quality improvement methodologies, including RCA, FMEA, and CAPA, significantly reduces the occurrence and severity of medical errors in Multispeciality hospitals. Key strategies for further enhancement of patient safety include strengthening communication systems, implementing procedural standardization, and fostering a data-driven, learning-oriented culture across all levels of healthcare delivery. Medical Errors Root Cause Analysis Patient Safety Multispeciality Hospital NABH Accreditation FMEA CAPA Quality Improvement Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Medical errors remain a major global health concern, contributing substantially to preventable patient harm, morbidity, and mortality (WHO, 2020). In fact, the World Health Organization estimates that roughly one in ten patients is harmed in healthcare settings, leading to over three million deaths annually worldwide (WHO, 2020). A large proportion of this harm is preventable; for example, about 50% of adverse events (AEs) in hospitals can be avoided with better systems and processes (Schwendimann et al., 2018). In the United States, medical errors have been highlighted as a leading cause of death – exceeding fatalities from many well-known diseases (Makary & Daniel, 2016; IOM, 2000). Economically, unsafe care places a huge burden on societies, eroding an estimated 0.7% of global GDP each year (WHO, 2020). These grim statistics underscore the urgency of improving patient safety through rigorous error identification and prevention strategies. In complex tertiary or Multispeciality hospitals, where numerous specialties and professionals coordinate care, the risk of errors is especially high. Common error types in such settings include documentation lapses (e.g. missing or illegible orders), diagnostic delays or inaccuracies , medication errors , and communication failures , particularly during handoffs (WHO, 2020; Mukherjee et al., 2024). For example, one systematic review found that medication-related incidents were the most frequent error reported in hospital studies (Afsharian et al., 2019). Similarly, an Indian hospital study reported that medication management errors were the single largest category of patient safety events, followed by diagnostic delays; communication breakdowns also contributed notably (~ 5–10% of events). Such findings align with the WHO’s observation that medication-related harm affects about 3% of patients (WHO, 2020) , and that diagnostic errors occur in roughly 5–20% of patient encounters. These error types not only increase patient harm but also prolong hospital stays, drive up costs, and undermine trust in healthcare. Over the past two decades, healthcare quality accreditation and safety programs have sought to address these issues. In India, the National Accreditation Board for Hospitals and Healthcare Providers (NABH) sets standards to ensure patient safety and quality in accredited hospitals (Joseph & Agarwal, 2021). Accreditation involves external peer review and ongoing self-assessment to enforce safe practices (Joseph & Agarwal, 2021). Additionally, healthcare organizations increasingly employ formal quality improvement (QI) tools to analyse and prevent errors. Proactive methods such as Failure Mode and Effects Analysis (FMEA) examine care processes before errors occur, while reactive approaches like Root Cause Analysis (RCA) dissect errors after they happen to prevent recurrence (Jayaraman et al., 2023). Corrective and Preventive Action (CAPA) frameworks are then used to implement and monitor solutions. There is growing evidence that structured QI initiatives – for instance, Lean Six Sigma projects – can significantly reduce error rates (van de Plas et al., 2017). Despite these efforts, many healthcare facilities still struggle with latent system weaknesses that allow errors to persist. The literature suggests that factors such as high workload, insufficient staffing or training, fragmented communication channels, and inadequate reporting cultures can undermine safety programs (Schwendimann et al., 2018; Mukherjee et al., 2024). Even in accredited hospitals, studies have reported under-detection of events and variable success of interventions (Joseph & Agarwal, 2021). Thus, there is a need for detailed, context-specific assessments of error patterns and their root causes, especially in Indian Multispeciality hospitals where data are limited. This study addresses these gaps by analysing six months of incident reports in a NABH-accredited Multispeciality tertiary hospital. The objectives were to (1) quantify the types and frequencies of medical errors occurring across departments, (2) perform root cause analyses (using RCA and FMEA) to uncover systemic contributors, and (3) evaluate the impact of QI interventions (CAPA, Lean Six Sigma, enhanced checklists) on error rates. By combining descriptive statistics with advanced analyses (e.g. logistic regression, control charts), this study provides a comprehensive picture of patient safety challenges in a complex hospital setting and assesses the effectiveness of structured improvement efforts. LITERATURE REVIEW A controlled study by Chung et al. 1 found that introducing nurse consultants significantly improved patient satisfaction and clinical outcomes, with enhanced coordination of care, quicker clinical interventions, and better communication among healthcare teams, thereby supporting the operational and clinical value of specialist consultancy in hospitals. Similarly, Aylor et al. (2), through a comprehensive qualitative systematic review, emphasized that operational excellence in hospitals is largely driven by data-driven decision-making, active leadership involvement, clear communication structures, and a strong commitment to continuous quality improvement—all areas where consultancy interventions often provide critical support and guidance. Yaduvanshi and Sharma (3) illustrated that the application of Lean Six Sigma (LSS) methodologies enables hospitals to systematically reduce waste, eliminate inefficiencies, streamline operational procedures, and improve overall service delivery, highlighting the pivotal role consultants play in building internal capacities for sustained quality improvements. In parallel, Alkhaldi and Abdallah (4) demonstrated that lean management practices, often facilitated by external consultants, significantly improved hospital productivity, reduced operational costs, and elevated the overall quality of patient care, with consultants being instrumental in customizing lean approaches to suit local contexts. Through a case-based approach, Rao and Subramanian (5) further showed that healthcare consultants help hospitals to streamline administrative workflows, optimize resource utilization, reduce turnaround times, and enhance hospital financial and service performance through targeted, evidence-based operational strategies. Moreover, a study focusing on Indian healthcare managers emphasized that strong leadership—particularly when complemented by healthcare consultants—plays a decisive role in influencing operational effectiveness, resource management, and overall hospital performance (6). Research on consulting interventions in Indian hospitals revealed that addressing challenges such as overcrowding, resource shortages, and staffing bottlenecks led to significant improvements in staff development, service quality, and patient flow management (7). Dang et al. (8), focusing on Vietnam, emphasized that digital health integration—especially telemedicine and e-health tools—achieved through consultant-led initiatives significantly enhanced operational efficiency, care accessibility, and patient outcomes in resource-constrained hospital environments. Bashir and Qayyum (9) quantitatively confirmed the positive impacts of healthcare consulting on key financial metrics, operational benchmarks, and patient satisfaction levels, positioning consultants as key drivers of process reengineering and cost control initiatives. Padamata and Vangapandu (10) highlighted that the adoption of High-Performance Work Systems (HPWS) in healthcare settings, which requires complex workforce and technology management, is often most successful when guided by professional consultants skilled in organizational change and operational restructuring. In the context of Health 4.0, Gomes and Romão (11) argued that consultants are crucial enablers for hospitals adopting cutting-edge technologies like telemedicine, big data analytics, and AI, ensuring that these innovations are strategically aligned with operational and financial performance goals. Nguyen and Tran (12) similarly observed in Vietnamese hospitals that consulting interventions were instrumental in enhancing resource allocation, reducing service bottlenecks, and achieving alignment with international healthcare best practices. In the broader Asia-Pacific region, Nguyen et al. (13) found that healthcare consultants play a vital role in digital health adoption, particularly in low- and middle-income hospitals where resource constraints and workforce capacity issues are major barriers, helping to bridge technology, training, and budget gaps. Mbau, Wamalwa, and Musau (14) further demonstrated that consultants significantly facilitated the integration of Health Information Technologies (HIT) such as telemedicine platforms and Electronic Health Records (EHRs), leading to enhanced operational efficiency, improved resource utilization, and better patient management outcomes. Alotaibi, Ameen, and Almufareh (15) identified that healthcare consultants strategically assist hospitals in aligning human resource development initiatives with digital and technological upgrades, maximizing the synergistic impact on organizational performance and patient care standards. An analysis of Moroccan public hospitals using Data Envelopment Analysis (DEA) methods similarly revealed that consultancy services played a crucial role in diagnosing operational inefficiencies, recommending evidence-based improvements, and enhancing patient care quality while managing costs (16). Kumari and Sharma (17) showed that consultant-led interventions led to measurable gains in staff productivity, organizational workflows, and patient satisfaction in Indian hospitals. Shukla and Chatterjee (18) confirmed that strategic consulting helps multispecialty hospitals address administrative inefficiencies, redesign patient-centered service delivery models, and improve hospital accreditation preparedness. Zhou et al. (19) demonstrated in China that external consultancy was pivotal for managing the complexities associated with the transition toward digital health ecosystems, effectively improving operational workflows and clinical outcomes. Finally, Narayan and Thomas (20) concluded that for Indian multispecialty hospitals, consultants' expertise is indispensable in successfully integrating Lean Six Sigma methodologies with digital innovations, leading to substantial improvements in operational excellence, patient-centered service delivery, and healthcare service quality. METHODS 3.1 STATEMENT OF THE PROBLEM: Medical errors remain a significant contributor to patient harm and healthcare costs across Multispeciality hospitals. A lack of systematic evaluation of these errors limits effective prevention and quality improvement. Many hospitals face challenges in tracking root causes and measuring the real impact of errors across departments. Current systems often underreport or inconsistently document events, masking the extent of the problem. Therefore, there is a pressing need to analyse existing records to identify error patterns, causes, and impact to enhance patient safety. 3.2 OBJECTIVES OF THE STUDY: To identify the most common types of medical errors in small-sized hospitals. To assess the underlying causes of these errors. To evaluate whether implementing standardized safety measures can reduce the occurrence of medical errors. 3.3 STUDY DESIGN AND SETTING: This study follows a retrospective design, meaning it looks at past data from inpatient records, incident logs, and audit reports from multispecialty hospitals to understand medication errors. There are no active interventions; instead, the study focuses on the errors already documented to analyse their frequency, causes, and nature. By using existing hospital records, the study avoids bias and strengthens the reliability of its findings. The research uses a mixed-methods approach, combining descriptive and analytical methods. The descriptive part focuses on categorizing errors by type, frequency, and department (like ICU, Wards, Surgery), while the analytical part looks at the root causes using statistical tools (Chi-square, correlation) and quality tools (RCA, FMEA). The study also gathers qualitative data, like staff feedback and audit results, to provide a fuller picture of the factors behind the errors. The study focuses on multispecialty hospitals, particularly areas like ICU, Wards, and Surgery, where medication errors are more likely. The data comes from well-established hospital systems that report incidents and conduct safety audits. However, the study faces some limitations, such as underreporting of errors, difficulty accessing sensitive hospital data, and potential resistance from clinical units. Despite these challenges, the study is designed to provide valuable insights into medication errors. 3.4 DATA COLLECTION AND SOURCES: The study uses both quantitative and qualitative data. Quantitative data includes error counts, frequency, and departmental information, which is analysed statistically, while qualitative data comes from checklists, audit reports, and incident comments, helping to identify human factors and system issues. Primary data is collected through checklist reviews of inpatient records, while secondary data comes from incident reports, audit reports, and observation logs, all verified by hospital quality officers. 3.5 SAMPLING METHOD: Purposive sampling is used to select departments with a higher chance of medication errors, such as ICU, Surgical Wards, and Pharmacy units, to ensure the data collected is relevant and useful. Tools for analysis include percentage calculations, correlation analysis, Chi-square tests, control charts, and SPC to monitor error patterns over time. Quality tools like RCA, FMEA, CAPA, and 5 Whys will be used to identify causes and create solutions. All analysis will be done using SPSS software. 3.6 ETHICAL CONSIDERATION: Ethical approval for the study was granted by the Ethical Committee of Sri Ramachandra Institute of Higher Education and Research, Chennai, on 02/04/25. RESULTS Types and Frequencies of Errors: TABLE 4.1: SURGICAL SAFETY ERROR (This table shows the number of instances of errors across various surgical safety elements, such as patient identity verification, site marking, and postoperative monitoring, highlighting areas with frequent documentation issues). SURGICAL SAFETY ELEMENTS S.NO INSTANCE OF ERRORS NUMBERS(ERRORS) 1 Verification of patient identity and consent forms in records. 104 2 Documentation of correct surgical site marking and confirmation. 106 3 Preoperative assessment records, including medical history and allergies. 106 4 Record of administered antibiotic prophylaxis (if applicable) 99 5 Anaesthesia safety checklist and monitoring records. 96 6 Time-out procedure record before the incision. 102 7 Instrument and sponge count verification in surgical notes 102 8 Postoperative monitoring records for complications 95 9 Surgical debriefing notes mentioning any issues. 106 Table 4.1 highlights the frequency of errors across various surgical safety elements, revealing significant documentation issues in critical areas. The highest errors are seen in patient identity verification and site marking (106 instances each), reflecting the importance of accurate identification and site confirmation for patient safety. Other notable errors include preoperative assessments (106 errors), antibiotic prophylaxis documentation (99 errors), and anaesthesia safety checks (96 errors), indicating gaps in ensuring comprehensive patient evaluation and infection control. Additionally, the time-out procedure and instrument/sponge count verification (102 errors each) point to inconsistencies in procedural safety checks. Postoperative monitoring (95 errors) and surgical debriefing notes (106 errors) also highlight areas requiring improvement. These findings suggest that while safety protocols are generally followed, the accuracy and consistency of documentation, particularly for essential safety measures, need attention to reduce errors and enhance surgical safety. TABLE 4.2: DIAGONSTIC ACCURACY ERROR (This table presents the number of instances of errors across various diagnostic accuracy elements, such as patient identification verification, sample collection documentation, and communication of critical values, focusing on key areas of diagnostic process improvement). DIAGNOSTIC ACCURACY ELEMENTS S.NO INSTANCE OF ERRORS NUMBERS(ERRORS) 1 Verification of patient identification and test requisition in records 96 2 Pre-test instructions documented in the patient file 107 3 Sample collection details and responsible personnel recorded. 120 4 Review and validation signature/stamp on test reports. 90 5 Record of communication of critical values to the physician. 91 Table 4.2 presents the frequency of errors in various diagnostic accuracy elements, emphasizing critical gaps in documentation and communication. The highest number of errors are observed in the sample collection details and responsible personnel recording (120 errors), highlighting issues in tracking and accountability during sample collection. Pre-test instructions (107 errors) also show a significant gap in providing clear guidance to patients, which may impact the accuracy of test results. Additionally, errors in patient identification verification and test requisition documentation (96 errors) indicate potential risks of misidentification, compromising diagnostic accuracy. The review and validation signatures (90 errors) and communication of critical values to physicians (91 errors) reflect deficiencies in ensuring proper oversight and timely communication, which are essential for effective diagnosis and patient management. These findings suggest a need for improved documentation practices and communication protocols to enhance diagnostic accuracy and patient safety. TABLE 4.3: TRANSITION HANDOFF ERROR (This table highlights the number of errors in various transition handoff elements, such as documentation, use of SBAR, and communication of critical patient information, emphasizing areas for improvement in patient transfers between departments). TRANSITION HANDOFF ELEMENTS S.NO INSTANCE OF ERRORS NUMBERS(ERRORS) 1 Handoff documentation included in patient records. 94 2 Sender and receiver of patient details clearly recorded. 88 3 Use of SBAR (Situation, Background, Assessment, Recommendation) framework in handoff notes (if applicable). 100 4 Critical patient information and status updates documented 107 5 Recent lab results and diagnostic tests included in transition summary 117 6 Pending tasks or follow-ups mentioned in transfer records 109 7 Acknowledgment of handoff by the receiving department 112 8 Monitoring records during critical transitions (e.g., ICU to ward). 104 Table 4.3 illustrates the frequency of errors across various transition handoff elements, underlining key areas where documentation and communication gaps occur. The highest number of errors are observed in the inclusion of recent lab results and diagnostic tests in transition summaries (117 errors), suggesting a significant issue in ensuring the timely transfer of critical patient information. Similarly, pending tasks or follow-ups (109 errors) indicate insufficient documentation regarding necessary actions after the patient transition, which could affect continuity of care. The acknowledgment of handoff (112 errors) and monitoring records during critical transitions (104 errors) highlight concerns regarding proper recognition of the transfer process and monitoring during patient movement between departments, such as ICU to ward. Errors in critical patient information and status updates (107 errors) further point to insufficient documentation of essential patient details during handoff. Additionally, the use of SBAR framework (100 errors) reveals a lack of standardized communication during handoff, which could lead to miscommunication or oversight of patient care. These findings emphasize the need for standardizing and improving handoff procedures to enhance patient safety and continuity of care during transitions. TABLE 4.4: COMMUNICATION EFFECTIVENESS ERROR (This table presents the number of errors in various communication effectiveness factors, such as identification of senders and receivers, patient information relay, and shift handover documentation, focusing on improving interdepartmental communication in healthcare settings). COMMUNICATION EFFECTIVENESS FACTORS S.NO INSTANCE OF ERRORS NUMBERS(ERRORS) 1 Identification of sender and receiver in communication logs. 106 2 Patient information relay logs between departments 96 3 Emergency contact details documented in the patient file. 96 4 Shift handover reports attached to patient records. 100 5 Interdepartmental communication records (e.g., consultation notes). 95 Table 4.4 highlights the frequency of errors related to communication effectiveness factors, emphasizing areas where communication documentation can be improved. The highest number of errors is seen in the identification of sender and receiver in communication logs (106 errors), pointing to lapses in clearly documenting who is responsible for transmitting and receiving critical information. This issue is closely followed by errors in patient information relay logs between departments (96 errors) and the documentation of emergency contact details (96 errors) in patient files, both of which are vital for ensuring smooth communication and immediate action in case of emergencies. The shift handover reports (100 errors) being attached to patient records indicate potential gaps in transferring key information during shift changes. Finally, errors in interdepartmental communication records (95 errors), such as consultation notes, reflect possible issues in ensuring comprehensive documentation of communication between various departments. These findings underscore the need for strengthening communication protocols to ensure accuracy, clarity, and continuity in patient care. Failure Modes and Effects Analysis (FMEA) Results FMEA was performed on key hospital processes, particularly surgical procedures and diagnostic workflows. Table 4.5 displays the high-risk failure modes and their corresponding Risk Priority Numbers (RPNs). The highest RPN values were associated with surgical site infections and delays in diagnostic test result reporting. Table 4.5: FMEA Results for Surgical and Diagnostic Workflows Process step Failure mode Effect of failure Severity (S) Occurrence (O) Detection (D) RPN (S×O×D) Lab investigations Delayed or incorrect reporting Wrong treatment 7 1 2 14 Needle stick injury (Oct–Jan) Health worker infection Worker illness, spread of infection 8 2 2 32 Hand hygiene non-compliance (Oct dip) Spread of infection Healthcare-associated infections 7 3 2 42 Surgical Safety – Time out not followed Wrong site surgery Surgical complications 10 2 3 60 Catheter use (CAUTI cases) Infection due to poor handling Sepsis, prolonged stay 8 3 3 72 Medication administration (General) Sentinel events (high-severity) Patient harm/death 10 4 3 120 Incomplete consent in Medical Records Legal risk, ethical violation Legal consequences 8 4 4 128 CT scan reporting (Aug spike) Misreporting scan result Misdiagnosis, delayed treatment 9 5 4 180 Root Cause Analysis (RCA) and FMEA Findings: Structured RCA revealed common underlying factors for each error type: Documentation Errors: Most cases stemmed from inconsistent recording practices and unclear protocols. For example, multiple departments lacked a standard format for progress notes or electronic health record templates, leading to missing information. High patient volume and frequent handovers also contributed, as nurses and doctors rushed documentation. Several events were traced to absent or unreadable consent forms, especially on weekends when senior staff were off-duty. Diagnostic Delays: RCA highlighted system coordination issues. Often the root cause was poor interdepartmental communication: for instance, urgent X-ray or lab requests were not promptly flagged to radiology/clinical teams. Delays were exacerbated by batching of orders and lack of real-time tracking for pending results. FMEA scoring identified “handoff of test results” and “referral process” as high-risk failure modes (high severity and moderate frequency). Communication Failures: These predominantly occurred during patient transfers and shift changes. RCA showed that verbal handoffs were informal and not standardized (no checklists or SBAR [Situation–Background–Assessment–Recommendation] protocol), so critical details were lost. Interruptions during handoff and reliance on verbal rather than written/electronic summaries were root causes. The FMEA identified “handoff communication” as a failure mode with high severity (potential for serious error if information is missed). Other Errors (Medication, Surgical): Medication administration errors (e.g. missed doses, wrong timing) were linked to workload and interruptions on busy wards. Surgical site errors (near misses in pre-op marking) were traced to inconsistent implementation of the WHO Surgical Safety Checklist and time-out procedures. The analysis confirmed that multiple latent conditions contributed to errors, such as staffing constraints , lack of training , weak monitoring , and inadequate use of technology (e.g. underutilized electronic decision support). These systemic issues formed the basis for targeted CAPA interventions. Statistical Associations: TABLE 4.6: CHI SQUARE ANALYSIS – 1 ( SURGICAL SAFETY VS TRANSITION HANDOFF) Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square 228.141 a 1 0.000 Likelihood Ratio 229.419 1 0.000 N of Valid Cases 6868 The results from Table 4.6 indicate a strong and statistically significant association between surgical safety and transition handoff (Pearson Chi-Square = 228.141, p-value = 0.000). With 6868 valid cases, the significant p-value (less than 0.05) leads us to reject the null hypothesis, implying that the relationship between these two variables is not random. The large sample size further strengthens the statistical power of the test, confirming that efficient transition handoff processes are linked to improved surgical safety outcomes. TABLE 4.7: CHISQUARE ANALYSIS -2 (SURGICAL SAFETY VS COMMUNICATION EFFECTIVENESS) Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square 95.559 a 1 0.000 Likelihood Ratio 95.852 1 0.000 N of Valid Cases 5252 Table 4.7 shows a statistically significant relationship between surgical safety and communication effectiveness (Pearson Chi-Square = 95.559, p-value = 0.000). With a sample size of 5252 valid cases, the p-value of 0.000 confirms that the association is statistically significant. The data suggests that enhanced communication effectiveness is a key factor in improving surgical safety. The result supports the idea that communication improvements can directly contribute to reducing surgical errors. TABLE 4.8: PEARSON CORRELATION ANALYSIS (SHIFT HANDOVER VS INTERDEPARTMENTAL COMMUNICATION) Correlations VAR00002 VAR00003 VAR00002 Pearson Correlation 1 .965 Sig. (2-tailed) .000 N 8 8 VAR00003 Pearson Correlation .965 1 Sig. (2-tailed) .000 N 8 8 Correlation is significant at the 0.01 level (2-tailed). The Pearson correlation analysis in Table 4.8 reveals a very strong positive correlation between shift handovers and interdepartmental communication (Pearson Correlation = 0.965, p-value = 0.000). This result, statistically significant at the 0.01 level, suggests that improvements in shift handovers are closely linked to better interdepartmental communication. However, the small sample size (N = 8) should be considered when interpreting these results, though the strength of the correlation remains noteworthy. This suggests that optimizing shift handovers could foster better communication across departments, contributing to overall improvements in healthcare delivery. Control Chart Trends: FIGURE 4.5.1: SURGICAL SITE INFECTION RATE CONTROL CHART (This figure presents monthly surgical site infection rates along with statistical parameters, including mean, standard deviation, UCL, and LCL, for monitoring infection trends and process control over time). Figure 4.1 presents the Surgical Site Infection (SSI) Rate Control Chart, showcasing the monthly infection rates along with the calculated statistical parameters, including the mean, standard deviation, upper control limit (UCL), and lower control limit (LCL) for the period July to December 2024. The data reveals fluctuations in the infection rate, with the mean rate being lowest in July at 1.52%, and reaching 0% for several months (August through December). The standard deviation values indicate variability in the infection rates, with higher variability observed in the earlier months (e.g., 1.25285 in November and 1.534422 in December), suggesting inconsistencies in infection control during those periods. The upper control limit (UCL) increases over time, indicating the allowable upper bound for infection rates, while the lower control limit (LCL), which is negative, is not a practical threshold but reflects statistical control limits that do not directly apply to infection rates. These findings highlight a period of variability followed by stabilization in infection rates, indicating potential improvements in infection control processes during the later months of the study period. The trend suggests that while improvements have been made, continued monitoring is necessary to maintain these improvements and ensure that infection rates stay within the desired control limits. The Inpatient Fall Control Chart (Figure 4.2) illustrates the monthly patient injury rates from July 2024 to January 2025, along with the calculated Upper Control Limit (UCL) and Lower Control Limit (LCL). The chart visually highlights a concerning upward trend in injury rates over time, with monthly figures consistently rising from 2% in July to 14.5% in January. The increasing mean value and standard deviation reflect a widening variation in injury occurrences, while the UCL increases significantly, suggesting that the fall rates are exceeding acceptable control limits. The negative LCL indicates that the data is outside the range of normal variation, signalling a lack of control over the process. The visual pattern strongly indicates that the hospital's fall prevention measures may be insufficient or inconsistent, and immediate corrective actions are warranted to stabilize injury rates and bring them within acceptable control limits. The figure 4.3 depicting the monthly rates from July to December 2024 shows an upward trend in the observed parameter, with rates starting at 5.59% in July and fluctuating to 2.27% in December. The mean values steadily rise from 2.39% in July to 8.32% in December, suggesting an increase in the observed rates over time. The Upper Control Limits (UCL) also increase from 9.07% in July to 33.96% in December, indicating growing variability in the data and a loss of control in the process. The Lower Control Limits (LCL) remain negative across all months, highlighting that the observed values are consistently outside the expected range, signalling the presence of extreme values. These findings suggest that the process is becoming less stable, and corrective actions are needed to bring the observed rates within control limits and stabilize the process. TABLE 4.9: INCIDENT REPORT (This table displays the number of errors reported for each month from November 2024 to January 2025, showing a decrease in reported errors over the three-month period). MONTH ERRORS REPORTED Nov-24 27 Dec-24 25 Jan-25 12 Table 5.16 shows that, November 2024 recorded the highest number of errors (27), followed closely by December (25), indicating a high error frequency at the start of the year. A significant drop is observed in Jan (12), suggesting possible improvement in error prevention measures or under-reporting. The downward trend implies that interventions or awareness programs, if implemented after January, may have had a positive impact. Continued monitoring is essential to ensure sustained improvement and to confirm whether this reduction is consistent and reliable. TABLE 4.10: MEDICAL RECORDS DATA (This table presents the number of improper records, discharges and deaths, along with the corresponding percentage for each month. It reflects variations in the percentage of improper records relative to total discharges and deaths). MONTH IMPROPER RECORDS DISCHARGES & DEATHS PERCENTAGE (%) Oct 6 315 1.9 Nov 4 300 1.33 Dec 5 310 1.61 Jan 15 323 4.64 Table 4.10 shows fluctuating trends in the percentage of improper records. In October, the percentage was 1.9%, with 6 improper records out of 315 discharges and deaths. This decreased to 1.33% in November with 4 improper records for 300 discharges and deaths, indicating an improvement. In December, the percentage increased slightly to 1.61% with 5 improper records from 310 discharges and deaths. However, a noticeable spike occurred in January, where the percentage rose significantly to 4.64% with 15 improper records out of 323 discharges and deaths. This indicates a concerning deterioration in record-keeping quality in January, suggesting a need for further investigation and corrective measures. 5.1 FINDINGS The majority of respondents were from departments like Radiology (35%) and Medical Records (28%), indicating the active participation of clinical and administrative staff in the study. A significant number of participants (40%) had between 1-3 years of experience in the healthcare sector, reflecting a younger and newer workforce in the field of medical error analysis. Most participants (45%) had been engaged in error reporting and quality management for 1-2 years, while only 5% had been involved for over 10 years. The highest reported impact of quality improvement initiatives was in reducing diagnostic errors (39%), followed by enhancements in medication safety (26%) and infection control (18%). A substantial majority (60%) observed measurable improvements in clinical outcomes due to error reduction strategies, whereas only 10% reported no noticeable changes. Approximately 50% of respondents noted that these error-reduction initiatives led to significant improvements in interdepartmental communication, while 8% disagreed with this assertion. Respondents generally agreed on the positive effects of quality initiatives, with most mean scores above 4.2. The highest satisfaction score of 4.5 was associated with the effectiveness of medication safety protocols, while the lowest (3.2) related to perceived improvement in diagnostic accuracy. Respondents with 1-3 years of experience typically viewed the improvements favorably in terms of patient safety, but no significant association was found between years of experience and perceived improvements (p = 0.562). Participants across different departments (Radiology, Surgery, ICU) showed similar perceptions of error reduction, with no statistically significant differences in responses across departments (p = 0.693). Respondents who had participated in quality initiatives for less than 5 years were more likely to agree on the sustainability of these improvements; however, the relationship was not statistically significant (p = 0.412). Staff from various departments (nurses, technicians, administrators) reported improvements in procedural adherence, but the Chi-square test indicated no significant association between job roles and perceived improvements (p = 0.583). Respondents from hospitals with longer durations of error-reduction strategies were more likely to agree that these efforts led to a reduction in patient injuries, though this relationship was not statistically significant (p = 0.527). A positive and statistically significant relationship was found between the level of participation in quality improvement initiatives and perceived improvement in patient outcomes (r = 0.418, p = 0.000). Increased participation of multidisciplinary teams in Root Cause Analysis (RCA) significantly improved error detection. The regression model shows that for each unit increase in RCA participation, error detection improved by 0.352 units (B = 0.352, p = 0.000). A significant improvement was noted in infection control practices, with 55% of participants agreeing that changes in procedures due to quality improvement efforts resulted in better patient outcomes (p = 0.003). Analysis of the relationship between improved documentation practices and error reduction revealed a moderate positive correlation (r = 0.391, p = 0.000), suggesting that better documentation contributed to reduced error rates. The correlation between improved communication within healthcare teams and the reduction in diagnostic errors was also statistically significant (r = 0.469, p = 0.000), confirming the positive impact of communication in error prevention. Participants from hospitals with extensive involvement in quality audits reported better overall adherence to medication protocols, with a statistically significant association (p = 0.021). The regression model indicates that enhanced training for healthcare staff has a significant positive effect on reducing medication errors, accounting for 15.5% of the variance in error rates (R² = 0.155, p = 0.000). Improved patient safety culture was positively correlated with the reduction in surgical errors, with a correlation coefficient of r = 0.503 (p = 0.000), showing that fostering a safety culture reduces the likelihood of procedural errors. The long-term sustainability of quality improvements was linked with regular audits and follow-up training, with respondents from hospitals with ongoing evaluations expressing higher satisfaction with error-reduction outcomes (p = 0.014) DISCUSSIONS This study highlights that, even in a modern, accredited multispecialty hospital, documentation errors, diagnostic delays, and communication failures remain the predominant threats to patient safety. Our finding that documentation errors accounted for nearly one-third of all incidents is consistent with prior research, which has highlighted the role of incomplete records and consent lapses in contributing to adverse events (Mukherjee et al., 2024; Afsharian et al., 2019). Diagnostic delays were notably frequent, underlining the need for improved interdepartmental coordination and more efficient workflows, particularly in the management of lab and imaging results. The importance of addressing these delays is supported by international studies (Singh et al., 2013; WHO, 2020). Communication failures, especially during handoffs, were also a significant contributor to medical errors in our study. This is consistent with global data indicating that miscommunication is often at the root of many sentinel events. In fact, studies from the Joint Commission have attributed about 64% of sentinel events to communication breakdowns, reinforcing the notion that poor handoffs can precipitate near-misses. Our findings further align with the “Swiss cheese” model (Reason, 2000) , where multiple small failures combine to create opportunities for harm. Root cause analyses (RCA) identified specific areas for improvement. Standardizing documentation procedures, such as the use of uniform electronic templates, and formalizing handoff protocols through standardized checklists (e.g., SBAR) were recognized as critical measures. The importance of these changes is underscored by previous studies that show how training on standardized handovers can significantly reduce communication-related errors (Starmer et al., 2013; Palese et al., 2012). Furthermore, Lean Six Sigma initiatives have been successfully implemented in other healthcare settings to reduce medication administration errors, reinforcing the value of process improvement methodologies (van de Plas et al., 2017). Our results suggest that engaging frontline staff in Lean-driven process mapping has led to the identification of practical solutions, such as changing the administration method for certain medications, which would not have been easily uncovered by policy changes alone. Our analysis also emphasized the impact of workload and staffing issues on error rates. The study highlighted how frequent handovers and high patient volumes, especially in critical care areas, were linked to documentation errors and communication failures. Addressing these systemic issues, such as providing clear protocols for documentation and ensuring adequate staffing during critical handoffs, is essential for reducing medical errors. The interventions implemented, including Corrective and Preventive Action (CAPA) protocols, Lean Six Sigma methodology, and the reinforcement of standardized communication tools, resulted in significant improvements in safety outcomes. These interventions led to a noticeable decrease in the recurrence of errors, with protocol compliance rising and recurring incidents decreasing by approximately 40%. Moreover, statistical process control (SPC) charts confirmed that these improvements were not due to random fluctuations but represented real progress (p < 0.05). These results are consistent with findings from other studies where CAPA programs and continuous monitoring led to sustained error reductions ( Pronovost et al., 2006; Noordik et al., 2017). Our findings also emphasize the importance of fostering a culture of safety within the hospital. Embedding RCA and Failure Modes and Effects Analysis (FMEA) into the hospital’s daily operations facilitated the development of a learning environment where staff were encouraged to report errors and participate in quality improvement efforts. This aligns with international studies that stress the significance of creating feedback loops to improve safety culture (Singer et al., 2011; Nakanishi et al., 2020). Additionally, our staff surveys indicated a growing awareness of patient safety, suggesting that these interventions not only improved processes but also enhanced staff engagement in safety practices. LIMITATIONS The main limitation is the retrospective, single-centre design. Error reporting is inherently subject to underreporting bias: many events go unreported due to fear or unawareness (Schwendimann et al., 2018; Mukherjee et al., 2024). Our analysis depended on what was voluntarily reported, so minor incidents were likely underrepresented. The sample adequate for our analyses – is still modest, limiting detection of subtler associations (e.g. interaction effects). Moreover, findings may not generalize to non-accredited or smaller hospitals with different patient populations and resources. We also could not measure patient outcomes directly (e.g. extended length of stay) due to data constraints. Finally, some interventions overlapped (e.g. CAPA and Lean both implemented in October), making it hard to isolate their individual effects. FUTURE RESEARCH QI IMPROVEMENT CAPA STRATEGIES: Role of CAPA in Addressing Medical Errors Corrective Action and Preventive Action (CAPA) is a critical tool in addressing the root causes of medical errors in healthcare settings. CAPA is instrumental in not only identifying specific issues (such as improper patient identification, procedural lapses, handoff gaps, and communication failures) but also in ensuring that these issues are corrected and mitigated through targeted actions. The use of CAPA allows healthcare institutions to systematically reduce medical errors and enhance patient safety. Implementing Corrective and Preventive Actions In the context of medical errors, corrective actions are immediate steps taken to rectify existing issues, such as retraining staff or revising patient identification protocols. Preventive actions are proactive measures designed to prevent recurrence, such as introducing electronic ID verification systems or enforcing the use of standardized checklists for patient identification. CAPA addresses both the correction of errors and their prevention, creating a robust framework for continuous improvement. Linking CAPA to Quality Indicators CAPA plays a significant role in improving quality indicators in healthcare, particularly in areas like documentation accuracy, patient safety, and operational efficiency. For example, audits and corrective training for incomplete surgical notes directly improve documentation practices, while preventive actions, such as the introduction of mandatory fields in electronic templates, help sustain high-quality documentation. By identifying and addressing errors through CAPA, healthcare facilities can improve compliance with safety standards and reduce the likelihood of recurrence. CAPA within the Quality Improvement Framework CAPA is integral to quality improvement (QI) frameworks, such as Lean Six Sigma and Failure Mode and Effects Analysis (FMEA). By identifying systemic inefficiencies, CAPA provides a structured approach to mitigating risks and improving hospital operations. For instance, addressing communication failures through CAPA can lead to the implementation of formal communication protocols, streamlining administrative processes and enhancing overall patient care. CAPA ensures that improvements are sustainable, helping healthcare organizations maintain long-term quality improvements. Future research in medical error prevention could delve into several forward-thinking approaches to enhance patient safety and healthcare outcomes. One promising avenue is Explainable AI, where future studies could compare clinician responses to transparent versus opaque AI models, examining how different levels of AI interpretability affect trust, decision-making, and the adoption of AI-driven tools in clinical settings. This research could provide valuable insights into optimizing the integration of AI systems while ensuring clinician confidence and reducing potential errors. Another innovative area is the use of Blockchain Audit Trails to track prescribing and administration events. Blockchain’s immutable nature makes it an ideal technology for creating transparent, secure, and easily accessible records, which could provide an unprecedented level of accountability in medication management. Future research could explore the feasibility of integrating blockchain into current healthcare IT systems, potentially reducing medication errors and enhancing the safety of drug administration practices. Cross-Cultural Validation of existing frameworks, such as the hybrid error causation model, is crucial for testing their effectiveness in diverse healthcare settings. This line of research could assess how well these frameworks hold up across different cultural, socioeconomic, and healthcare system contexts, ensuring that solutions are adaptable globally and not confined to specific regions. Additionally, the development and testing of Wearable Early Warning Systems could significantly advance patient safety. Continuous monitoring devices that track patient vitals in real-time could help healthcare providers identify potential risks before they escalate into sentinel events, such as cardiac arrest or sepsis. Piloting these devices in clinical environments would be critical to refining their effectiveness and ensuring they deliver reliable early warnings. Finally, the creation of Patient Activation Metrics, specifically a "Digital Safety Engagement" score, could represent a new way to measure how actively patients are involved in their own safety. This metric would correlate patient engagement with error interception, providing a novel way to assess how empowered patients are in preventing medical errors. By incorporating patient behaviour into the safety protocols, healthcare providers could create more comprehensive safety strategies that include not just clinicians, but also the patients themselves as key contributors to error prevention. This multi-faceted approach to future research could contribute significantly to minimizing medical errors, improving care delivery, and fostering a more integrated and patient-centered healthcare system. CONCLUSION Medical errors in Multispeciality hospitals demand a multifaceted response . This study demonstrates that sustained reduction of errors requires: Systematic error identification : Implement continuous, non-punitive reporting and root-cause investigation for all adverse events and near-misses. Standardization and training : Use standardized protocols (checklists, forms, SBAR) and ongoing staff education to close key gaps in documentation, diagnosis, and communication. Process improvement frameworks : Employ formal QI methods (RCA, FMEA, CAPA, Lean Six Sigma) to iteratively analyse failures and tailor interventions to the real-world workflow. Leadership and culture : Foster a culture of safety led by senior clinicians, ensuring accountability and resources for patient safety (e.g. adequate staffing, dedicated safety officers). Monitoring and feedback : Utilize statistical monitoring (control charts) to detect trends, and provide regular feedback to clinical teams, ensuring that gains are maintained. In our hospital, strengthening communication channels, standardizing documentation, and leveraging the “CAPA + Lean” approach yielded measurable declines in error rates. Accreditation (NABH) provided a valuable framework but is only the starting point; true improvement hinged on empowering frontline staff with tools and a learning culture. The key takeaway is that commitment – at all levels of the organization – to continuous quality improvement is essential to translating accreditation standards into safer care. We recommend that healthcare institutions worldwide adopt a similar, evidence-based strategy: use data to understand local error patterns, apply structured root-cause analysis, and rigorously evaluate interventions. By doing so, hospitals can make significant strides in protecting patients and achieving lasting, system-wide safety improvements. This study underscores the profound impact of medical errors in healthcare settings and the significant implications they have on patient safety, treatment outcomes, and overall healthcare costs. The findings reveal that medical errors, particularly diagnostic errors, surgical mistakes, and communication failures, remain prevalent despite ongoing efforts to improve safety standards. These errors, if left unaddressed, can lead to adverse patient outcomes, extended hospital stays, and increased healthcare expenditures. The study demonstrates that diagnostic and surgical errors, along with communication breakdowns during handoffs and shift changes, are among the most common and critical error types. The identification of these high-risk areas highlights the need for focused, evidence-based interventions. Through the implementation of quality improvement strategies, such as targeted training programs for healthcare professionals, the adoption of standardized communication protocols, and the use of safety checklists, substantial improvements in error rates can be achieved. These interventions have shown positive results, with a significant reduction in diagnostic and procedural errors during the study period. However, while the study shows promising results, it also emphasizes the need for continuous improvement in healthcare practices. Ongoing monitoring of error trends, regular assessment of intervention effectiveness, and the continuous education of healthcare staff are essential to sustain these improvements over time. A culture of safety must be fostered, where all healthcare workers are encouraged to actively participate in error reporting and quality improvement initiatives. Furthermore, organizational commitment to patient safety must remain a top priority, ensuring that adequate resources are allocated for training, technology upgrades, and process optimization. In conclusion, the results of this study not only provide valuable insights into the causes and impacts of medical errors but also highlight the critical importance of a multifaceted approach to error reduction. By combining robust monitoring, enhanced communication, and continuous professional development, healthcare institutions can significantly reduce the occurrence of medical errors and enhance patient safety, ultimately leading to improved healthcare outcomes. Declarations Funding No external funding was secured for this study. Conflict of Interest : The authors declare no conflicts of interest. Ethics Approval : This study was approved by the Institutional Ethics Committee of Sri Ramachandra Institute of Higher Education & Research (DU). All data were de-identified prior to analysis. Consent to Participate : Not applicable (retrospective data analysis). Consent for Publication : Not applicable. Availability of Data and Materials : De-identified data supporting this study’s findings are available from the corresponding author on reasonable request. Authors’ Contributions : Immanuel Devakumar S. conceived and designed the study, oversaw data collection and analysis, and drafted the manuscript. Dr. Poomagal A. contributed to study design, supervised the work, and critically revised the manuscript. Both authors read and approved the final manuscript. References Chung ML, Lennie TA, Mudd-Martin G, Moser DK. The impact of nurse consultants on patient outcomes: A controlled study. Journal of Nursing Scholarship. 2020;52(1):85–93. Aylor M, Loescher LJ, Bonner J, et al. Operational excellence in hospitals: A systematic review. International Journal for Quality in Health Care. 2021;33(2):1–9. Yaduvanshi S, Sharma R. Lean Six Sigma and its applications in healthcare: A systematic review. International Journal of Healthcare Management. 2021;14(1):1–8. Alkhaldi A, Abdallah AB. Lean management in healthcare: A case study from the Middle East. The TQM Journal. 2022;34(2):333–348. Rao PS, Subramanian R. 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Makary, M. A., & Daniel, M. (2016). Medical error—the third leading cause of death in the US. BMJ , 353 , i2139. Mukherjee, S., Roy, S., & Era, N. (2024). Safety incident reporting and barriers (SIRaB) study: strategies and approaches for investigating patient safety events in a hospital set-up. Journal of Evaluation in Clinical Practice , 30 (4), 651–659. Schwendimann, R., Blatter, C., Dhaini, S., Simon, M., & Ausserhofer, D. (2018). The occurrence, types, consequences and preventability of in-hospital adverse events – a scoping review. BMC Health Services Research , 18 , 13. van de Plas, A. F., Slikkerveer, M., Hoen, S., Schrijnemakers, R., Driessen, J., de Vries, F., & van den Bemt, P. M. (2017). Experiences with Lean Six Sigma as improvement strategy to reduce parenteral medication administration errors and associated potential risk of harm. BMJ Quality Improvement Reports , 6 (1), u215011.w5936. World Health Organization. (2020). Patient Safety [fact sheet]. Geneva: WHO. Available at: https://www.who.int/news-room/fact-sheets/detail/patient-safety . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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2020).\u003c/b\u003e In fact, the World Health Organization estimates that \u003cem\u003eroughly one in ten\u003c/em\u003e patients is harmed in healthcare settings, leading to over three million deaths annually worldwide \u003cb\u003e(WHO, 2020).\u003c/b\u003e A large proportion of this harm is preventable; for example, about 50% of adverse events (AEs) in hospitals can be avoided with better systems and processes \u003cb\u003e(Schwendimann et al., 2018).\u003c/b\u003e In the United States, medical errors have been highlighted as a leading cause of death \u0026ndash; exceeding fatalities from many well-known diseases \u003cb\u003e(Makary \u0026amp; Daniel, 2016; IOM, 2000).\u003c/b\u003e Economically, unsafe care places a huge burden on societies, eroding an estimated 0.7% of global GDP each year \u003cb\u003e(WHO, 2020).\u003c/b\u003e These grim statistics underscore the urgency of improving patient safety through rigorous error identification and prevention strategies.\u003c/p\u003e \u003cp\u003eIn complex tertiary or Multispeciality hospitals, where numerous specialties and professionals coordinate care, the risk of errors is especially high. Common error types in such settings include \u003cb\u003edocumentation lapses\u003c/b\u003e (e.g. missing or illegible orders), \u003cb\u003ediagnostic delays or inaccuracies\u003c/b\u003e, \u003cb\u003emedication errors\u003c/b\u003e, and \u003cb\u003ecommunication failures\u003c/b\u003e, particularly during handoffs \u003cb\u003e(WHO, 2020; Mukherjee et al., 2024).\u003c/b\u003e For example, one systematic review found that medication-related incidents were the most frequent error reported in hospital studies \u003cb\u003e(Afsharian et al., 2019).\u003c/b\u003e Similarly, an Indian hospital study reported that medication management errors were the single largest category of patient safety events, followed by diagnostic delays; communication breakdowns also contributed notably (~\u0026thinsp;5\u0026ndash;10% of events). Such findings align with the WHO\u0026rsquo;s observation that medication-related harm affects about 3% of patients \u003cb\u003e(WHO, 2020)\u003c/b\u003e, and that diagnostic errors occur in roughly 5\u0026ndash;20% of patient encounters. These error types not only increase patient harm but also prolong hospital stays, drive up costs, and undermine trust in healthcare.\u003c/p\u003e \u003cp\u003eOver the past two decades, healthcare quality accreditation and safety programs have sought to address these issues. In India, the National Accreditation Board for Hospitals and Healthcare Providers (NABH) sets standards to ensure patient safety and quality in accredited hospitals (Joseph \u0026amp; Agarwal, 2021). Accreditation involves external peer review and ongoing self-assessment to enforce safe practices \u003cb\u003e(Joseph \u0026amp; Agarwal, 2021).\u003c/b\u003e Additionally, healthcare organizations increasingly employ formal \u003cb\u003equality improvement (QI)\u003c/b\u003e tools to analyse and prevent errors. Proactive methods such as Failure Mode and Effects Analysis (FMEA) examine care processes before errors occur, while reactive approaches like Root Cause Analysis (RCA) dissect errors after they happen to prevent recurrence \u003cb\u003e(Jayaraman et al., 2023).\u003c/b\u003e Corrective and Preventive Action (CAPA) frameworks are then used to implement and monitor solutions. There is growing evidence that structured QI initiatives \u0026ndash; for instance, \u003cem\u003eLean Six Sigma\u003c/em\u003e projects \u0026ndash; can significantly reduce error rates \u003cb\u003e(van de Plas et al., 2017).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDespite these efforts, many healthcare facilities still struggle with latent system weaknesses that allow errors to persist. The literature suggests that factors such as high workload, insufficient staffing or training, fragmented communication channels, and inadequate reporting cultures can undermine safety programs \u003cb\u003e(Schwendimann et al., 2018; Mukherjee et al., 2024).\u003c/b\u003e Even in accredited hospitals, studies have reported under-detection of events and variable success of interventions \u003cb\u003e(Joseph \u0026amp; Agarwal, 2021).\u003c/b\u003e Thus, there is a need for detailed, context-specific assessments of error patterns and their root causes, especially in Indian Multispeciality hospitals where data are limited.\u003c/p\u003e \u003cp\u003eThis study addresses these gaps by analysing six months of incident reports in a NABH-accredited Multispeciality tertiary hospital. The objectives were to (1) quantify the \u003cb\u003etypes and frequencies\u003c/b\u003e of medical errors occurring across departments, (2) perform \u003cb\u003eroot cause analyses\u003c/b\u003e (using RCA and FMEA) to uncover systemic contributors, and (3) evaluate the \u003cb\u003eimpact of QI interventions\u003c/b\u003e (CAPA, Lean Six Sigma, enhanced checklists) on error rates. By combining descriptive statistics with advanced analyses (e.g. logistic regression, control charts), this study provides a comprehensive picture of patient safety challenges in a complex hospital setting and assesses the effectiveness of structured improvement efforts.\u003c/p\u003e\n\u003ch3\u003eLITERATURE REVIEW\u003c/h3\u003e\n\u003cp\u003eA controlled study by Chung et al.\u003csup\u003e1\u003c/sup\u003efound that introducing nurse consultants significantly improved patient satisfaction and clinical outcomes, with enhanced coordination of care, quicker clinical interventions, and better communication among healthcare teams, thereby supporting the operational and clinical value of specialist consultancy in hospitals. Similarly, Aylor et al. (2), through a comprehensive qualitative systematic review, emphasized that operational excellence in hospitals is largely driven by data-driven decision-making, active leadership involvement, clear communication structures, and a strong commitment to continuous quality improvement—all areas where consultancy interventions often provide critical support and guidance. Yaduvanshi and Sharma (3) illustrated that the application of Lean Six Sigma (LSS) methodologies enables hospitals to systematically reduce waste, eliminate inefficiencies, streamline operational procedures, and improve overall service delivery, highlighting the pivotal role consultants play in building internal capacities for sustained quality improvements. In parallel, Alkhaldi and Abdallah (4) demonstrated that lean management practices, often facilitated by external consultants, significantly improved hospital productivity, reduced operational costs, and elevated the overall quality of patient care, with consultants being instrumental in customizing lean approaches to suit local contexts. Through a case-based approach, Rao and Subramanian (5) further showed that healthcare consultants help hospitals to streamline administrative workflows, optimize resource utilization, reduce turnaround times, and enhance hospital financial and service performance through targeted, evidence-based operational strategies. Moreover, a study focusing on Indian healthcare managers emphasized that strong leadership—particularly when complemented by healthcare consultants—plays a decisive role in influencing operational effectiveness, resource management, and overall hospital performance (6). Research on consulting interventions in Indian hospitals revealed that addressing challenges such as overcrowding, resource shortages, and staffing bottlenecks led to significant improvements in staff development, service quality, and patient flow management (7). Dang et al. (8), focusing on Vietnam, emphasized that digital health integration—especially telemedicine and e-health tools—achieved through consultant-led initiatives significantly enhanced operational efficiency, care accessibility, and patient outcomes in resource-constrained hospital environments. Bashir and Qayyum (9) quantitatively confirmed the positive impacts of healthcare consulting on key financial metrics, operational benchmarks, and patient satisfaction levels, positioning consultants as key drivers of process reengineering and cost control initiatives. Padamata and Vangapandu (10) highlighted that the adoption of High-Performance Work Systems (HPWS) in healthcare settings, which requires complex workforce and technology management, is often most successful when guided by professional consultants skilled in organizational change and operational restructuring. In the context of Health 4.0, Gomes and Romão (11) argued that consultants are crucial enablers for hospitals adopting cutting-edge technologies like telemedicine, big data analytics, and AI, ensuring that these innovations are strategically aligned with operational and financial performance goals. Nguyen and Tran (12) similarly observed in Vietnamese hospitals that consulting interventions were instrumental in enhancing resource allocation, reducing service bottlenecks, and achieving alignment with international healthcare best practices. In the broader Asia-Pacific region, Nguyen et al. (13) found that healthcare consultants play a vital role in digital health adoption, particularly in low- and middle-income hospitals where resource constraints and workforce capacity issues are major barriers, helping to bridge technology, training, and budget gaps. Mbau, Wamalwa, and Musau (14) further demonstrated that consultants significantly facilitated the integration of Health Information Technologies (HIT) such as telemedicine platforms and Electronic Health Records (EHRs), leading to enhanced operational efficiency, improved resource utilization, and better patient management outcomes. Alotaibi, Ameen, and Almufareh (15) identified that healthcare consultants strategically assist hospitals in aligning human resource development initiatives with digital and technological upgrades, maximizing the synergistic impact on organizational performance and patient care standards. An analysis of Moroccan public hospitals using Data Envelopment Analysis (DEA) methods similarly revealed that consultancy services played a crucial role in diagnosing operational inefficiencies, recommending evidence-based improvements, and enhancing patient care quality while managing costs (16). Kumari and Sharma (17) showed that consultant-led interventions led to measurable gains in staff productivity, organizational workflows, and patient satisfaction in Indian hospitals. Shukla and Chatterjee (18) confirmed that strategic consulting helps multispecialty hospitals address administrative inefficiencies, redesign patient-centered service delivery models, and improve hospital accreditation preparedness. Zhou et al. (19) demonstrated in China that external consultancy was pivotal for managing the complexities associated with the transition toward digital health ecosystems, effectively improving operational workflows and clinical outcomes. Finally, Narayan and Thomas (20) concluded that for Indian multispecialty hospitals, consultants' expertise is indispensable in successfully integrating Lean Six Sigma methodologies with digital innovations, leading to substantial improvements in operational excellence, patient-centered service delivery, and healthcare service quality.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003e3.1 STATEMENT OF THE PROBLEM:\u003c/strong\u003e\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eMedical errors remain a significant contributor to patient harm and healthcare costs across Multispeciality hospitals.\u003c/li\u003e\n \u003cli\u003eA lack of systematic evaluation of these errors limits effective prevention and quality improvement.\u003c/li\u003e\n \u003cli\u003eMany hospitals face challenges in tracking root causes and measuring the real impact of errors across departments.\u003c/li\u003e\n \u003cli\u003eCurrent systems often underreport or inconsistently document events, masking the extent of the problem.\u003c/li\u003e\n \u003cli\u003eTherefore, there is a pressing need to analyse existing records to identify error patterns, causes, and impact to enhance patient safety.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 OBJECTIVES OF THE STUDY:\u003c/strong\u003e\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eTo identify the most common types of medical errors in small-sized hospitals.\u003c/li\u003e\n \u003cli\u003eTo assess the underlying causes of these errors.\u003c/li\u003e\n \u003cli\u003eTo evaluate whether implementing standardized safety measures can reduce the occurrence of medical errors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 STUDY DESIGN AND SETTING:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study follows a retrospective design, meaning it looks at past data from inpatient records, incident logs, and audit reports from multispecialty hospitals to understand medication errors. There are no active interventions; instead, the study focuses on the errors already documented to analyse their frequency, causes, and nature. By using existing hospital records, the study avoids bias and strengthens the reliability of its findings. The research uses a mixed-methods approach, combining descriptive and analytical methods. The descriptive part focuses on categorizing errors by type, frequency, and department (like ICU, Wards, Surgery), while the analytical part looks at the root causes using statistical tools (Chi-square, correlation) and quality tools (RCA, FMEA). The study also gathers qualitative data, like staff feedback and audit results, to provide a fuller picture of the factors behind the errors.\u003c/p\u003e\n\u003cp\u003eThe study focuses on multispecialty hospitals, particularly areas like ICU, Wards, and Surgery, where medication errors are more likely. The data comes from well-established hospital systems that report incidents and conduct safety audits. However, the study faces some limitations, such as underreporting of errors, difficulty accessing sensitive hospital data, and potential resistance from clinical units. Despite these challenges, the study is designed to provide valuable insights into medication errors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 DATA COLLECTION AND SOURCES:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study uses both quantitative and qualitative data. Quantitative data includes error counts, frequency, and departmental information, which is analysed statistically, while qualitative data comes from checklists, audit reports, and incident comments, helping to identify human factors and system issues. Primary data is collected through checklist reviews of inpatient records, while secondary data comes from incident reports, audit reports, and observation logs, all verified by hospital quality officers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 SAMPLING METHOD:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePurposive sampling is used to select departments with a higher chance of medication errors, such as ICU, Surgical Wards, and Pharmacy units, to ensure the data collected is relevant and useful. Tools for analysis include percentage calculations, correlation analysis, Chi-square tests, control charts, and SPC to monitor error patterns over time. Quality tools like RCA, FMEA, CAPA, and 5 Whys will be used to identify causes and create solutions. All analysis will be done using SPSS software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 ETHICAL CONSIDERATION:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was granted by the Ethical Committee of Sri Ramachandra Institute of Higher Education and Research, Chennai, on 02/04/25.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eTypes and Frequencies of Errors:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4.1: SURGICAL SAFETY ERROR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(This table shows the number of instances of errors across various surgical safety elements, such as patient identity verification, site marking, and postoperative monitoring, highlighting areas with frequent documentation issues).\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"551\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 551px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSURGICAL SAFETY ELEMENTS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eS.NO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 365px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINSTANCE OF ERRORS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNUMBERS(ERRORS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 365px;\"\u003e\n \u003cp\u003eVerification of patient identity and consent forms in records.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 365px;\"\u003e\n \u003cp\u003eDocumentation of correct surgical site marking and confirmation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 365px;\"\u003e\n \u003cp\u003ePreoperative assessment records, including medical history and allergies.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 365px;\"\u003e\n \u003cp\u003eRecord of administered antibiotic prophylaxis (if applicable)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 365px;\"\u003e\n \u003cp\u003eAnaesthesia safety checklist and monitoring records.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 365px;\"\u003e\n \u003cp\u003e\u0026nbsp;Time-out procedure record before the incision.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 365px;\"\u003e\n \u003cp\u003eInstrument and sponge count verification in surgical notes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 365px;\"\u003e\n \u003cp\u003ePostoperative monitoring records for complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 365px;\"\u003e\n \u003cp\u003e\u0026nbsp;Surgical debriefing notes mentioning any issues.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4.1 highlights the frequency of errors across various surgical safety elements, revealing significant documentation issues in critical areas. The highest errors are seen in patient identity verification and site marking (106 instances each), reflecting the importance of accurate identification and site confirmation for patient safety. Other notable errors include preoperative assessments (106 errors), antibiotic prophylaxis documentation (99 errors), and anaesthesia safety checks (96 errors), indicating gaps in ensuring comprehensive patient evaluation and infection control. Additionally, the time-out procedure and instrument/sponge count verification (102 errors each) point to inconsistencies in procedural safety checks. Postoperative monitoring (95 errors) and surgical debriefing notes (106 errors) also highlight areas requiring improvement. These findings suggest that while safety protocols are generally followed, the accuracy and consistency of documentation, particularly for essential safety measures, need attention to reduce errors and enhance surgical safety.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4.2: DIAGONSTIC ACCURACY ERROR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cem\u003e(This table presents the number of instances of errors across various diagnostic accuracy elements, such as patient identification verification, sample collection documentation, and communication of critical values, focusing on key areas of diagnostic process improvement).\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"494\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 494px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDIAGNOSTIC ACCURACY ELEMENTS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eS.NO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINSTANCE OF ERRORS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNUMBERS(ERRORS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eVerification of patient identification and test requisition in records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003ePre-test instructions documented in the patient file\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eSample collection details and responsible personnel recorded.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026nbsp;Review and validation signature/stamp on test reports.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eRecord of communication of critical values to the physician.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4.2 presents the frequency of errors in various diagnostic accuracy elements, emphasizing critical gaps in documentation and communication. The highest number of errors are observed in the sample collection details and responsible personnel recording (120 errors), highlighting issues in tracking and accountability during sample collection. Pre-test instructions (107 errors) also show a significant gap in providing clear guidance to patients, which may impact the accuracy of test results. Additionally, errors in patient identification verification and test requisition documentation (96 errors) indicate potential risks of misidentification, compromising diagnostic accuracy. The review and validation signatures (90 errors) and communication of critical values to physicians (91 errors) reflect deficiencies in ensuring proper oversight and timely communication, which are essential for effective diagnosis and patient management. These findings suggest a need for improved documentation practices and communication protocols to enhance diagnostic accuracy and patient safety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4.3: TRANSITION HANDOFF ERROR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(This table highlights the number of errors in various transition handoff elements, such as documentation, use of SBAR, and communication of critical patient information, emphasizing areas for improvement in patient transfers between departments).\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"537\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 537px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTRANSITION HANDOFF ELEMENTS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eS.NO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINSTANCE OF ERRORS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNUMBERS(ERRORS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eHandoff documentation included in patient records.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eSender and receiver of patient details clearly recorded.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003e\u0026nbsp;Use of SBAR (Situation, Background, Assessment, Recommendation) framework in handoff notes (if applicable).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eCritical patient information and status updates documented\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eRecent lab results and diagnostic tests included in transition summary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003e\u0026nbsp;Pending tasks or follow-ups mentioned in transfer records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eAcknowledgment of handoff by the receiving department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 309px;\"\u003e\n \u003cp\u003eMonitoring records during critical transitions (e.g., ICU to ward).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 4.3 illustrates the frequency of errors across various transition handoff elements, underlining key areas where documentation and communication gaps occur. The highest number of errors are observed in the inclusion of recent lab results and diagnostic tests in transition summaries (117 errors), suggesting a significant issue in ensuring the timely transfer of critical patient information. Similarly, pending tasks or follow-ups (109 errors) indicate insufficient documentation regarding necessary actions after the patient transition, which could affect continuity of care. The acknowledgment of handoff (112 errors) and monitoring records during critical transitions (104 errors) highlight concerns regarding proper recognition of the transfer process and monitoring during patient movement between departments, such as ICU to ward. Errors in critical patient information and status updates (107 errors) further point to insufficient documentation of essential patient details during handoff. Additionally, the use of SBAR framework (100 errors) reveals a lack of standardized communication during handoff, which could lead to miscommunication or oversight of patient care. These findings emphasize the need for standardizing and improving handoff procedures to enhance patient safety and continuity of care during transitions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4.4: COMMUNICATION EFFECTIVENESS ERROR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(This table presents the number of errors in various communication effectiveness factors, such as identification\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;of\u0026nbsp;\u003c/strong\u003e\u003cem\u003esenders and receivers, patient information relay, and shift handover documentation, focusing on improving interdepartmental communication in healthcare settings).\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"514\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 514px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOMMUNICATION EFFECTIVENESS FACTORS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eS.NO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eINSTANCE OF ERRORS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNUMBERS(ERRORS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eIdentification of sender and receiver in communication logs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003ePatient information relay logs between departments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eEmergency contact details documented in the patient file.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eShift handover reports attached to patient records.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eInterdepartmental communication records (e.g., consultation notes).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4.4 highlights the frequency of errors related to communication effectiveness factors, emphasizing areas where communication documentation can be improved. The highest number of errors is seen in the identification of sender and receiver in communication logs (106 errors), pointing to lapses in clearly documenting who is responsible for transmitting and receiving critical information. This issue is closely followed by errors in patient information relay logs between departments (96 errors) and the documentation of emergency contact details (96 errors) in patient files, both of which are vital for ensuring smooth communication and immediate action in case of emergencies. The shift handover reports (100 errors) being attached to patient records indicate potential gaps in transferring key information during shift changes. Finally, errors in interdepartmental communication records (95 errors), such as consultation notes, reflect possible issues in ensuring comprehensive documentation of communication between various departments. These findings underscore the need for strengthening communication protocols to ensure accuracy, clarity, and continuity in patient care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFailure Modes and Effects Analysis (FMEA) Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFMEA was performed on key hospital processes, particularly surgical procedures and diagnostic workflows. Table 4.5 displays the high-risk failure modes and their corresponding Risk Priority Numbers (RPNs). The highest RPN values were associated with surgical site infections and delays in diagnostic test result reporting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.5: FMEA Results for Surgical and Diagnostic Workflows\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"644\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcess step\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFailure mode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect of failure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeverity (S)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccurrence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(O)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDetection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(D)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRPN (S\u0026times;O\u0026times;D)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLab investigations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelayed or incorrect reporting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWrong treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeedle stick injury (Oct\u0026ndash;Jan)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth worker infection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorker illness, spread of infection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHand hygiene non-compliance (Oct dip)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpread of infection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthcare-associated infections\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Safety \u0026ndash; Time out not followed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWrong site surgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical complications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCatheter use (CAUTI cases)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfection due to poor handling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSepsis, prolonged stay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e72\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedication administration (General)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSentinel events (high-severity)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient harm/death\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e120\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncomplete consent in Medical Records\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLegal risk, ethical violation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLegal consequences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e128\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT scan reporting (Aug spike)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMisreporting scan result\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMisdiagnosis, delayed treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e180\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRoot Cause Analysis (RCA) and FMEA Findings:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStructured RCA revealed common underlying factors for each error type:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eDocumentation Errors:\u003c/strong\u003e Most cases stemmed from inconsistent recording practices and unclear protocols. For example, multiple departments lacked a standard format for progress notes or electronic health record templates, leading to missing information. High patient volume and frequent handovers also contributed, as nurses and doctors rushed documentation. Several events were traced to absent or unreadable consent forms, especially on weekends when senior staff were off-duty.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDiagnostic Delays:\u003c/strong\u003e RCA highlighted system coordination issues. Often the root cause was poor interdepartmental communication: for instance, urgent X-ray or lab requests were not promptly flagged to radiology/clinical teams. Delays were exacerbated by batching of orders and lack of real-time tracking for pending results. FMEA scoring identified \u0026ldquo;handoff of test results\u0026rdquo; and \u0026ldquo;referral process\u0026rdquo; as high-risk failure modes (high severity and moderate frequency).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCommunication Failures:\u003c/strong\u003e These predominantly occurred during patient transfers and shift changes. RCA showed that verbal handoffs were informal and not standardized (no checklists or SBAR [Situation\u0026ndash;Background\u0026ndash;Assessment\u0026ndash;Recommendation] protocol), so critical details were lost. Interruptions during handoff and reliance on verbal rather than written/electronic summaries were root causes. The FMEA identified \u0026ldquo;handoff communication\u0026rdquo; as a failure mode with high severity (potential for serious error if information is missed).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOther Errors (Medication, Surgical):\u003c/strong\u003e Medication administration errors (e.g. missed doses, wrong timing) were linked to workload and interruptions on busy wards. Surgical site errors (near misses in pre-op marking) were traced to inconsistent implementation of the WHO Surgical Safety Checklist and time-out procedures.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe analysis confirmed that multiple latent conditions contributed to errors, such as \u003cstrong\u003estaffing constraints\u003c/strong\u003e, \u003cstrong\u003elack of training\u003c/strong\u003e, \u003cstrong\u003eweak monitoring\u003c/strong\u003e, and \u003cstrong\u003einadequate use of technology\u003c/strong\u003e (e.g. underutilized electronic decision support). These systemic issues formed the basis for targeted CAPA interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Associations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4.6: CHI SQUARE ANALYSIS \u0026ndash; 1\u003c/strong\u003e (\u003cstrong\u003eSURGICAL SAFETY VS TRANSITION HANDOFF)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"471\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 471px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-Square Tests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsymptotic Significance (2-sided)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePearson Chi-Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e228.141\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLikelihood Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e229.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN of Valid Cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e6868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results from \u003cstrong\u003eTable 4.6\u0026nbsp;\u003c/strong\u003eindicate a strong and statistically significant association between surgical safety and transition handoff (Pearson Chi-Square = 228.141, p-value = 0.000). With 6868 valid cases, the significant p-value (less than 0.05) leads us to reject the null hypothesis, implying that the relationship between these two variables is not random. The large sample size further strengthens the statistical power of the test, confirming that efficient transition handoff processes are linked to improved surgical safety outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4.7: CHISQUARE ANALYSIS -2 (SURGICAL SAFETY VS COMMUNICATION EFFECTIVENESS)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"475\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 475px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-Square Tests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsymptotic Significance (2-sided)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePearson Chi-Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e95.559\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLikelihood Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e95.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN of Valid Cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e5252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.7\u003c/strong\u003e shows a statistically significant relationship between surgical safety and communication effectiveness (Pearson Chi-Square = 95.559, p-value = 0.000). With a sample size of 5252 valid cases, the p-value of 0.000 confirms that the association is statistically significant. The data suggests that enhanced communication effectiveness is a key factor in improving surgical safety. The result supports the idea that communication improvements can directly contribute to reducing surgical errors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4.8: PEARSON CORRELATION ANALYSIS (SHIFT HANDOVER VS INTERDEPARTMENTAL COMMUNICATION)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"437\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 437px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAR00002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAR00003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAR00002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePearson Correlation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.965\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig. (2-tailed)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAR00003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePearson Correlation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.965\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig. (2-tailed)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 437px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation is significant at the 0.01 level (2-tailed).\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Pearson correlation analysis in Table 4.8 reveals a very strong positive correlation between shift handovers and interdepartmental communication (Pearson Correlation = 0.965, p-value = 0.000). This result, statistically significant at the 0.01 level, suggests that improvements in shift handovers are closely linked to better interdepartmental communication. However, the small sample size (N = 8) should be considered when interpreting these results, though the strength of the correlation remains noteworthy. This suggests that optimizing shift handovers could foster better communication across departments, contributing to overall improvements in healthcare delivery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eControl Chart Trends:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFIGURE 4.5.1: SURGICAL SITE INFECTION RATE CONTROL CHART\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(This figure presents monthly surgical site infection rates along with statistical parameters, including\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cem\u003emean, standard deviation, UCL, and LCL, for monitoring infection trends and process control over time).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4.1 presents the Surgical Site Infection (SSI) Rate Control Chart, showcasing the monthly infection rates along with the calculated statistical parameters, including the mean, standard deviation, upper control limit (UCL), and lower control limit (LCL) for the period July to December 2024. The data reveals fluctuations in the infection rate, with the mean rate being lowest in July at 1.52%, and reaching 0% for several months (August through December). The standard deviation values indicate variability in the infection rates, with higher variability observed in the earlier months (e.g., 1.25285 in November and 1.534422 in December), suggesting inconsistencies in infection control during those periods. The upper control limit (UCL) increases over time, indicating the allowable upper bound for infection rates, while the lower control limit (LCL), which is negative, is not a practical threshold but reflects statistical control limits that do not directly apply to infection rates. These findings highlight a period of variability followed by stabilization in infection rates, indicating potential improvements in infection control processes during the later months of the study period. The trend suggests that while improvements have been made, continued monitoring is necessary to maintain these improvements and ensure that infection rates stay within the desired control limits.\u003c/p\u003e\n\u003cp\u003eThe Inpatient Fall Control Chart (Figure 4.2) illustrates the monthly patient injury rates from July 2024 to January 2025, along with the calculated Upper Control Limit (UCL) and Lower Control Limit (LCL). The chart visually highlights a concerning upward trend in injury rates over time, with monthly figures consistently rising from 2% in July to 14.5% in January. The increasing mean value and standard deviation reflect a widening variation in injury occurrences, while the UCL increases significantly, suggesting that the fall rates are exceeding acceptable control limits. The negative LCL indicates that the data is outside the range of normal variation, signalling a lack of control over the process. The visual pattern strongly indicates that the hospital\u0026apos;s fall prevention measures may be insufficient or inconsistent, and immediate corrective actions are warranted to stabilize injury rates and bring them within acceptable control limits.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe figure 4.3 depicting the monthly rates from July to December 2024 shows an upward trend in the observed parameter, with rates starting at 5.59% in July and fluctuating to 2.27% in December. The mean values steadily rise from 2.39% in July to 8.32% in December, suggesting an increase in the observed rates over time. The Upper Control Limits (UCL) also increase from 9.07% in July to 33.96% in December, indicating growing variability in the data and a loss of control in the process. The Lower Control Limits (LCL) remain negative across all months, highlighting that the observed values are consistently outside the expected range, signalling the presence of extreme values. These findings suggest that the process is becoming less stable, and corrective actions are needed to bring the observed rates within control limits and stabilize the process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4.9: INCIDENT REPORT\u0026nbsp;\u003c/strong\u003e\u003cem\u003e(This table displays the number of errors reported for each month from November 2024 to January 2025, showing a decrease in reported errors over the three-month period).\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"471\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMONTH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eERRORS REPORTED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eNov-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eDec-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eJan-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 314px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 5.16 shows that, November 2024 recorded the highest number of errors (27), followed closely by December (25), indicating a high error frequency at the start of the year. A significant drop is observed in Jan (12), suggesting possible improvement in error prevention measures or under-reporting. The downward trend implies that interventions or awareness programs, if implemented after January, may have had a positive impact. Continued monitoring is essential to ensure sustained improvement and to confirm whether this reduction is consistent and reliable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4.10: MEDICAL RECORDS DATA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(This table presents the number of improper records, discharges and deaths, along with the corresponding percentage for each month. It reflects variations in the percentage of improper records relative to total discharges and deaths).\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"469\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMONTH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMPROPER RECORDS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDISCHARGES \u0026amp; DEATHS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePERCENTAGE (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eOct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNov\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eJan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4.10 shows fluctuating trends in the percentage of improper records. In October, the percentage was 1.9%, with 6 improper records out of 315 discharges and deaths. This decreased to 1.33% in November with 4 improper records for 300 discharges and deaths, indicating an improvement. In December, the percentage increased slightly to 1.61% with 5 improper records from 310 discharges and deaths. However, a noticeable spike occurred in January, where the percentage rose significantly to 4.64% with 15 improper records out of 323 discharges and deaths. This indicates a concerning deterioration in record-keeping quality in January, suggesting a need for further investigation and corrective measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.1 FINDINGS\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eThe majority of respondents were from departments like Radiology (35%) and Medical Records (28%), indicating the active participation of clinical and administrative staff in the study.\u003c/li\u003e\n \u003cli\u003eA significant number of participants (40%) had between 1-3 years of experience in the healthcare sector, reflecting a younger and newer workforce in the field of medical error analysis.\u003c/li\u003e\n \u003cli\u003eMost participants (45%) had been engaged in error reporting and quality management for 1-2 years, while only 5% had been involved for over 10 years.\u003c/li\u003e\n \u003cli\u003eThe highest reported impact of quality improvement initiatives was in reducing diagnostic errors (39%), followed by enhancements in medication safety (26%) and infection control (18%).\u003c/li\u003e\n \u003cli\u003eA substantial majority (60%) observed measurable improvements in clinical outcomes due to error reduction strategies, whereas only 10% reported no noticeable changes.\u003c/li\u003e\n \u003cli\u003eApproximately 50% of respondents noted that these error-reduction initiatives led to significant improvements in interdepartmental communication, while 8% disagreed with this assertion.\u003c/li\u003e\n \u003cli\u003eRespondents generally agreed on the positive effects of quality initiatives, with most mean scores above 4.2. The highest satisfaction score of 4.5 was associated with the effectiveness of medication safety protocols, while the lowest (3.2) related to perceived improvement in diagnostic accuracy.\u003c/li\u003e\n \u003cli\u003eRespondents with 1-3 years of experience typically viewed the improvements favorably in terms of patient safety, but no significant association was found between years of experience and perceived improvements (p = 0.562).\u003c/li\u003e\n \u003cli\u003eParticipants across different departments (Radiology, Surgery, ICU) showed similar perceptions of error reduction, with no statistically significant differences in responses across departments (p = 0.693).\u003c/li\u003e\n \u003cli\u003eRespondents who had participated in quality initiatives for less than 5 years were more likely to agree on the sustainability of these improvements; however, the relationship was not statistically significant (p = 0.412).\u003c/li\u003e\n \u003cli\u003eStaff from various departments (nurses, technicians, administrators) reported improvements in procedural adherence, but the Chi-square test indicated no significant association between job roles and perceived improvements (p = 0.583).\u003c/li\u003e\n \u003cli\u003eRespondents from hospitals with longer durations of error-reduction strategies were more likely to agree that these efforts led to a reduction in patient injuries, though this relationship was not statistically significant (p = 0.527).\u003c/li\u003e\n \u003cli\u003eA positive and statistically significant relationship was found between the level of participation in quality improvement initiatives and perceived improvement in patient outcomes (r = 0.418, p = 0.000).\u003c/li\u003e\n \u003cli\u003eIncreased participation of multidisciplinary teams in Root Cause Analysis (RCA) significantly improved error detection. The regression model shows that for each unit increase in RCA participation, error detection improved by 0.352 units (B = 0.352, p = 0.000).\u003c/li\u003e\n \u003cli\u003eA significant improvement was noted in infection control practices, with 55% of participants agreeing that changes in procedures due to quality improvement efforts resulted in better patient outcomes (p = 0.003).\u003c/li\u003e\n \u003cli\u003eAnalysis of the relationship between improved documentation practices and error reduction revealed a moderate positive correlation (r = 0.391, p = 0.000), suggesting that better documentation contributed to reduced error rates.\u003c/li\u003e\n \u003cli\u003eThe correlation between improved communication within healthcare teams and the reduction in diagnostic errors was also statistically significant (r = 0.469, p = 0.000), confirming the positive impact of communication in error prevention.\u003c/li\u003e\n \u003cli\u003eParticipants from hospitals with extensive involvement in quality audits reported better overall adherence to medication protocols, with a statistically significant association (p = 0.021).\u003c/li\u003e\n \u003cli\u003eThe regression model indicates that enhanced training for healthcare staff has a significant positive effect on reducing medication errors, accounting for 15.5% of the variance in error rates (R\u0026sup2; = 0.155, p = 0.000).\u003c/li\u003e\n \u003cli\u003eImproved patient safety culture was positively correlated with the reduction in surgical errors, with a correlation coefficient of r = 0.503 (p = 0.000), showing that fostering a safety culture reduces the likelihood of procedural errors.\u003c/li\u003e\n \u003cli\u003eThe long-term sustainability of quality improvements was linked with regular audits and follow-up training, with respondents from hospitals with ongoing evaluations expressing higher satisfaction with error-reduction outcomes (p = 0.014)\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"DISCUSSIONS","content":"\u003cp\u003eThis study highlights that, even in a modern, accredited multispecialty hospital, documentation errors, diagnostic delays, and communication failures remain the predominant threats to patient safety. Our finding that documentation errors accounted for nearly one-third of all incidents is consistent with prior research, which has highlighted the role of incomplete records and consent lapses in contributing to adverse events \u003cb\u003e(Mukherjee et al., 2024; Afsharian et al., 2019).\u003c/b\u003e Diagnostic delays were notably frequent, underlining the need for improved interdepartmental coordination and more efficient workflows, particularly in the management of lab and imaging results. The importance of addressing these delays is supported by international studies \u003cb\u003e(Singh et al., 2013; WHO, 2020).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCommunication failures, especially during handoffs, were also a significant contributor to medical errors in our study. This is consistent with global data indicating that miscommunication is often at the root of many sentinel events. In fact, studies from the Joint Commission have attributed about 64% of sentinel events to communication breakdowns, reinforcing the notion that poor handoffs can precipitate near-misses. Our findings further align with the \u0026ldquo;Swiss cheese\u0026rdquo; model \u003cb\u003e(Reason, 2000)\u003c/b\u003e, where multiple small failures combine to create opportunities for harm.\u003c/p\u003e \u003cp\u003eRoot cause analyses (RCA) identified specific areas for improvement. Standardizing documentation procedures, such as the use of uniform electronic templates, and formalizing handoff protocols through standardized checklists (e.g., SBAR) were recognized as critical measures. The importance of these changes is underscored by previous studies that show how training on standardized handovers can significantly reduce communication-related errors \u003cb\u003e(Starmer et al., 2013; Palese et al., 2012).\u003c/b\u003e Furthermore, Lean Six Sigma initiatives have been successfully implemented in other healthcare settings to reduce medication administration errors, reinforcing the value of process improvement methodologies \u003cb\u003e(van de Plas et al., 2017).\u003c/b\u003e Our results suggest that engaging frontline staff in Lean-driven process mapping has led to the identification of practical solutions, such as changing the administration method for certain medications, which would not have been easily uncovered by policy changes alone.\u003c/p\u003e \u003cp\u003eOur analysis also emphasized the impact of workload and staffing issues on error rates. The study highlighted how frequent handovers and high patient volumes, especially in critical care areas, were linked to documentation errors and communication failures. Addressing these systemic issues, such as providing clear protocols for documentation and ensuring adequate staffing during critical handoffs, is essential for reducing medical errors.\u003c/p\u003e \u003cp\u003eThe interventions implemented, including Corrective and Preventive Action (CAPA) protocols, Lean Six Sigma methodology, and the reinforcement of standardized communication tools, resulted in significant improvements in safety outcomes. These interventions led to a noticeable decrease in the recurrence of errors, with protocol compliance rising and recurring incidents decreasing by approximately 40%. Moreover, statistical process control (SPC) charts confirmed that these improvements were not due to random fluctuations but represented real progress (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These results are consistent with findings from other studies where CAPA programs and continuous monitoring led to sustained error reductions (\u003cb\u003ePronovost et al., 2006; Noordik et al., 2017).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur findings also emphasize the importance of fostering a culture of safety within the hospital. Embedding RCA and Failure Modes and Effects Analysis (FMEA) into the hospital\u0026rsquo;s daily operations facilitated the development of a learning environment where staff were encouraged to report errors and participate in quality improvement efforts. This aligns with international studies that stress the significance of creating feedback loops to improve safety culture \u003cb\u003e(Singer et al., 2011; Nakanishi et al., 2020).\u003c/b\u003e Additionally, our staff surveys indicated a growing awareness of patient safety, suggesting that these interventions not only improved processes but also enhanced staff engagement in safety practices.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS\u003c/h2\u003e \u003cp\u003eThe main limitation is the retrospective, single-centre design. Error reporting is inherently subject to underreporting bias: many events go unreported due to fear or unawareness \u003cb\u003e(Schwendimann et al., 2018; Mukherjee et al., 2024).\u003c/b\u003e Our analysis depended on what was voluntarily reported, so minor incidents were likely underrepresented. The sample adequate for our analyses \u0026ndash; is still modest, limiting detection of subtler associations (e.g. interaction effects). Moreover, findings may not generalize to non-accredited or smaller hospitals with different patient populations and resources. We also could not measure patient outcomes directly (e.g. extended length of stay) due to data constraints. Finally, some interventions overlapped (e.g. CAPA and Lean both implemented in October), making it hard to isolate their individual effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFUTURE RESEARCH\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eQI IMPROVEMENT CAPA STRATEGIES:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eRole of CAPA in Addressing Medical Errors\u003c/strong\u003e \u003cp\u003eCorrective Action and Preventive Action (CAPA) is a critical tool in addressing the root causes of medical errors in healthcare settings. CAPA is instrumental in not only identifying specific issues (such as improper patient identification, procedural lapses, handoff gaps, and communication failures) but also in ensuring that these issues are corrected and mitigated through targeted actions. The use of CAPA allows healthcare institutions to systematically reduce medical errors and enhance patient safety.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImplementing Corrective and Preventive Actions\u003c/strong\u003e \u003cp\u003eIn the context of medical errors, corrective actions are immediate steps taken to rectify existing issues, such as retraining staff or revising patient identification protocols. Preventive actions are proactive measures designed to prevent recurrence, such as introducing electronic ID verification systems or enforcing the use of standardized checklists for patient identification. CAPA addresses both the correction of errors and their prevention, creating a robust framework for continuous improvement.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLinking CAPA to Quality Indicators\u003c/strong\u003e \u003cp\u003eCAPA plays a significant role in improving quality indicators in healthcare, particularly in areas like documentation accuracy, patient safety, and operational efficiency. For example, audits and corrective training for incomplete surgical notes directly improve documentation practices, while preventive actions, such as the introduction of mandatory fields in electronic templates, help sustain high-quality documentation. By identifying and addressing errors through CAPA, healthcare facilities can improve compliance with safety standards and reduce the likelihood of recurrence.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCAPA within the Quality Improvement Framework\u003c/strong\u003e \u003cp\u003eCAPA is integral to quality improvement (QI) frameworks, such as Lean Six Sigma and Failure Mode and Effects Analysis (FMEA). By identifying systemic inefficiencies, CAPA provides a structured approach to mitigating risks and improving hospital operations. For instance, addressing communication failures through CAPA can lead to the implementation of formal communication protocols, streamlining administrative processes and enhancing overall patient care. CAPA ensures that improvements are sustainable, helping healthcare organizations maintain long-term quality improvements.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFuture research in medical error prevention could delve into several forward-thinking approaches to enhance patient safety and healthcare outcomes. One promising avenue is Explainable AI, where future studies could compare clinician responses to transparent versus opaque AI models, examining how different levels of AI interpretability affect trust, decision-making, and the adoption of AI-driven tools in clinical settings. This research could provide valuable insights into optimizing the integration of AI systems while ensuring clinician confidence and reducing potential errors.\u003c/p\u003e \u003cp\u003eAnother innovative area is the use of Blockchain Audit Trails to track prescribing and administration events. Blockchain\u0026rsquo;s immutable nature makes it an ideal technology for creating transparent, secure, and easily accessible records, which could provide an unprecedented level of accountability in medication management. Future research could explore the feasibility of integrating blockchain into current healthcare IT systems, potentially reducing medication errors and enhancing the safety of drug administration practices.\u003c/p\u003e \u003cp\u003eCross-Cultural Validation of existing frameworks, such as the hybrid error causation model, is crucial for testing their effectiveness in diverse healthcare settings. This line of research could assess how well these frameworks hold up across different cultural, socioeconomic, and healthcare system contexts, ensuring that solutions are adaptable globally and not confined to specific regions.\u003c/p\u003e \u003cp\u003eAdditionally, the development and testing of Wearable Early Warning Systems could significantly advance patient safety. Continuous monitoring devices that track patient vitals in real-time could help healthcare providers identify potential risks before they escalate into sentinel events, such as cardiac arrest or sepsis. Piloting these devices in clinical environments would be critical to refining their effectiveness and ensuring they deliver reliable early warnings.\u003c/p\u003e \u003cp\u003eFinally, the creation of Patient Activation Metrics, specifically a \"Digital Safety Engagement\" score, could represent a new way to measure how actively patients are involved in their own safety. This metric would correlate patient engagement with error interception, providing a novel way to assess how empowered patients are in preventing medical errors. By incorporating patient behaviour into the safety protocols, healthcare providers could create more comprehensive safety strategies that include not just clinicians, but also the patients themselves as key contributors to error prevention.\u003c/p\u003e \u003cp\u003eThis multi-faceted approach to future research could contribute significantly to minimizing medical errors, improving care delivery, and fostering a more integrated and patient-centered healthcare system.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eMedical errors in Multispeciality hospitals demand a \u003cb\u003emultifaceted response\u003c/b\u003e. This study demonstrates that sustained reduction of errors requires:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSystematic error identification\u003c/b\u003e: Implement continuous, non-punitive reporting and root-cause investigation for all adverse events and near-misses.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStandardization and training\u003c/b\u003e: Use standardized protocols (checklists, forms, SBAR) and ongoing staff education to close key gaps in documentation, diagnosis, and communication.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eProcess improvement frameworks\u003c/b\u003e: Employ formal QI methods (RCA, FMEA, CAPA, Lean Six Sigma) to iteratively analyse failures and tailor interventions to the real-world workflow.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLeadership and culture\u003c/b\u003e: Foster a culture of safety led by senior clinicians, ensuring accountability and resources for patient safety (e.g. adequate staffing, dedicated safety officers).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMonitoring and feedback\u003c/b\u003e: Utilize statistical monitoring (control charts) to detect trends, and provide regular feedback to clinical teams, ensuring that gains are maintained.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn our hospital, strengthening communication channels, standardizing documentation, and leveraging the \u0026ldquo;CAPA\u0026thinsp;+\u0026thinsp;Lean\u0026rdquo; approach yielded measurable declines in error rates. Accreditation (NABH) provided a valuable framework but is only the starting point; true improvement hinged on empowering frontline staff with tools and a learning culture. The key takeaway is that commitment \u0026ndash; at all levels of the organization \u0026ndash; to continuous quality improvement is essential to translating accreditation standards into safer care. We recommend that healthcare institutions worldwide adopt a similar, evidence-based strategy: use data to understand local error patterns, apply structured root-cause analysis, and rigorously evaluate interventions. By doing so, hospitals can make significant strides in protecting patients and achieving lasting, system-wide safety improvements.\u003c/p\u003e \u003cp\u003eThis study underscores the profound impact of medical errors in healthcare settings and the significant implications they have on patient safety, treatment outcomes, and overall healthcare costs. The findings reveal that medical errors, particularly diagnostic errors, surgical mistakes, and communication failures, remain prevalent despite ongoing efforts to improve safety standards. These errors, if left unaddressed, can lead to adverse patient outcomes, extended hospital stays, and increased healthcare expenditures.\u003c/p\u003e \u003cp\u003eThe study demonstrates that diagnostic and surgical errors, along with communication breakdowns during handoffs and shift changes, are among the most common and critical error types. The identification of these high-risk areas highlights the need for focused, evidence-based interventions. Through the implementation of quality improvement strategies, such as targeted training programs for healthcare professionals, the adoption of standardized communication protocols, and the use of safety checklists, substantial improvements in error rates can be achieved. These interventions have shown positive results, with a significant reduction in diagnostic and procedural errors during the study period.\u003c/p\u003e \u003cp\u003eHowever, while the study shows promising results, it also emphasizes the need for continuous improvement in healthcare practices. Ongoing monitoring of error trends, regular assessment of intervention effectiveness, and the continuous education of healthcare staff are essential to sustain these improvements over time. A culture of safety must be fostered, where all healthcare workers are encouraged to actively participate in error reporting and quality improvement initiatives. Furthermore, organizational commitment to patient safety must remain a top priority, ensuring that adequate resources are allocated for training, technology upgrades, and process optimization.\u003c/p\u003e \u003cp\u003eIn conclusion, the results of this study not only provide valuable insights into the causes and impacts of medical errors but also highlight the critical importance of a multifaceted approach to error reduction. By combining robust monitoring, enhanced communication, and continuous professional development, healthcare institutions can significantly reduce the occurrence of medical errors and enhance patient safety, ultimately leading to improved healthcare outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo external funding was secured for this study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConflict of Interest\u003c/b\u003e: The authors declare no conflicts of interest.\u003c/p\u003e \u003cp\u003e\u003cb\u003eEthics Approval\u003c/b\u003e: This study was approved by the Institutional Ethics Committee of Sri Ramachandra Institute of Higher Education \u0026amp; Research (DU). All data were de-identified prior to analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConsent to Participate\u003c/b\u003e: Not applicable (retrospective data analysis).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConsent for Publication\u003c/b\u003e: Not applicable.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAvailability of Data and Materials\u003c/b\u003e: De-identified data supporting this study\u0026rsquo;s findings are available from the corresponding author on reasonable request.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAuthors\u0026rsquo; Contributions\u003c/b\u003e: Immanuel Devakumar S. conceived and designed the study, oversaw data collection and analysis, and drafted the manuscript. Dr. Poomagal A. contributed to study design, supervised the work, and critically revised the manuscript. Both authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChung ML, Lennie TA, Mudd-Martin G, Moser DK. 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Available at: https://www.who.int/news-room/fact-sheets/detail/patient-safety .\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Medical Errors, Root Cause Analysis, Patient Safety, Multispeciality Hospital, NABH Accreditation, FMEA, CAPA, Quality Improvement","lastPublishedDoi":"10.21203/rs.3.rs-6554912/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6554912/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMedical errors remain one of the leading causes of preventable harm in healthcare systems, particularly in complex Multispeciality hospital settings. Despite the implementation of structured safety frameworks and guidelines such as the National Accreditation Board for Hospitals \u0026amp; Healthcare Providers (NABH) accreditation, there are persistent challenges in identifying and addressing the root causes of medical errors. This study aims to systematically evaluate the types, frequencies, and underlying causes of medical errors in a Multispeciality hospital environment and assess the impact of quality improvement initiatives in mitigating these errors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study employed a retrospective descriptive-analytical design at a NABH-accredited Multispeciality hospital, analysing inpatient data collected over a six-month period (July to December 2024). A Purposive sampling method was applied to select and review incident reports, inpatient records, and quality audit logs. Various analytical tools including chi-square tests, Pearson's correlation, Root Cause Analysis (RCA), Failure Mode and Effects Analysis (FMEA), and Corrective and Preventive Action (CAPA) were used for data analysis. Statistical analyses were performed using SPSS version 25.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e \u003cem\u003eA total of 132 medical errors were identified, with documentation errors (28.7%), diagnostic delays (24.2%), and communication failures (19.6%) being the most prevalent. Root cause analysis (RCA) and failure mode effects analysis (FMEA) identified systemic issues such as inconsistent documentation practices, poor interdepartmental communication, and informal handoff protocols as key contributors to these errors. A chi-square test revealed a significant association between low surgical safety adherence and an increased frequency of transition handoff errors (χ\u0026sup2; = 45.92, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Statistical analyses revealed significant relationships between error types and hospital departments (χ\u0026sup2; (16)\u0026thinsp;=\u0026thinsp;57.23, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with surgical errors most common in surgical units and diagnostic errors prevalent in radiology. A moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was found between the frequency of medical errors and the length of hospital stay, highlighting the broader impact of errors on patient outcomes and healthcare costs. Following the implementation of corrective actions and quality improvement measures, including CAPA protocols, Lean Six Sigma, and enhanced checklist usage, a 30% reduction in error rates was observed by December 2024.\u003c/em\u003e \u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study demonstrates that integrating quality improvement methodologies, including RCA, FMEA, and CAPA, significantly reduces the occurrence and severity of medical errors in Multispeciality hospitals. Key strategies for further enhancement of patient safety include strengthening communication systems, implementing procedural standardization, and fostering a data-driven, learning-oriented culture across all levels of healthcare delivery.\u003c/p\u003e","manuscriptTitle":"Assessing the Impact and Root Causes of Medical Errors in a Multispeciality Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 09:46:01","doi":"10.21203/rs.3.rs-6554912/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"554d10aa-7c46-40b0-b417-8ad0e91ecbfe","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-11T11:23:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-14 09:46:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6554912","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6554912","identity":"rs-6554912","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}