Quality of Medical Records in Sudanese Public Hospitals During Armed Conflict: A Multi-Centre Cross-Sectional Study

Quality of Medical Records in Sudanese Public Hospitals During Armed Conflict: A Multi-Centre Cross-Sectional Study | 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 Quality of Medical Records in Sudanese Public Hospitals During Armed Conflict: A Multi-Centre Cross-Sectional Study Tebyan Abdalgader Abdallah Mohmmed, Malaz Mohamed, Mohanned Salman, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8370650/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: Accurate and complete medical records are essential for effective health service delivery, patient safety, and quality improvement. However, deficiencies in documentation remain a challenge in many low-resource settings. This study aimed to assess the completeness and readability of medical records in public hospitals across Sudan, providing evidence to inform health system strengthening. Methods: A cross-sectional study was conducted in October 2023, reviewing 604 medical records from surgical departments in six public hospitals across Sudan. Records were evaluated for completeness using a standardised checklist covering five domains: socio-demographic data, patient history, investigations and management, operation sheet notes, and handwriting clarity. Descriptive statistics were used to summarise findings, and one-way ANOVA was applied to compare completeness rates between hospitals and departments. Results: The overall completeness rate of medical records was 55.68% (SD ± 20). Socio-demographic data were 66.7% complete, patient history 39.23%, investigations and management 55.30%, and operation sheet notes 63.59%. Handwriting was readable in 60.3% of records. Significant differences in completeness were observed between hospitals and departments (p < 0.05). Conclusions: Substantial deficiencies in documentation and legibility were identified in Sudanese public hospitals, with notable variation across institutions and departments. Targeted interventions, such as standardised templates and electronic health records, are needed to improve documentation quality and support better health service delivery. Medical records Documentation quality Completeness Readability Public hospitals Sudan Surgery Patient safety Health information management Figures Figure 1 Background A medical record is a comprehensive account of a patient’s health history, including past and present illnesses and treatments, documented by healthcare professionals [ 1 ]. Accurate medical record-keeping is essential for delivering high-quality patient care, preventing delays and complications, and supporting medical research and education [ 2 , 3 ]. Furthermore, well-maintained records serve as a primary defence for healthcare professionals against malpractice claims [ 4 ]. Despite these critical roles, numerous studies have reported deficiencies in medical record documentation worldwide, including in Sudan [ 5 – 9 ]. Contributing factors include heavy workloads, time constraints, involvement of multiple care providers, limited knowledge and experience, and, importantly, the perception that documentation is not an integral part of treatment [ 10 , 11 ]. Strategies such as audits with feedback, reminders, templates, and multifaceted interventions have been proposed to address these gaps [ 12 ]. Evidence from countries such as China and Brazil indicates frequent omissions in medical records, including incomplete documentation of symptoms, non-drug treatments, and diagnoses [ 13 , 14 ]. Similarly, a retrospective study in Ethiopia found that only 18.4% of medical records met national standards, highlighting significant deficiencies in completeness and legibility, particularly in health centres compared to hospitals [ 6 ]. In Sudan, many discharge summaries fail to follow the structured, predesigned format, resulting in inadequate and inconsistent health information [ 15 , 16 ]. Additionally, studies have revealed insufficient knowledge and poor documentation practices among nurses, underscoring the need for in-service training [ 17 ]. Although the importance of medical record systems is increasingly recognised, most Sudanese studies have been small-scale and limited to Khartoum State, failing to reflect the national situation. This study aims to assess the quality of medical records—specifically their completeness and readability—across multiple states and to compare documentation quality between hospitals and departments. The findings will provide a foundation for improving medical record practices in Sudanese hospitals. Methods Study design and setting We conducted a cross-sectional, multi-centre study in October 2023 to assess the quality of medical records in surgical departments across six public hospitals in Sudan: Marawi, Atbara, Port Sudan, El-Obeid, El-Hasahisa, and Kosti Teaching Hospitals. These hospitals are in major cities representing different states (North, East, Central, and South). Selection was based on accessibility and safety during the ongoing armed conflict. Participants and eligibility criteria Medical records were obtained from the statistics departments of the selected hospitals. Inclusion criteria were records of patients admitted to surgical wards who underwent surgical procedures during October 2023. Records of patients admitted but not operated on were excluded. Sample size and sampling The sample size was calculated using Epi Info™ for population surveys, assuming a 95% confidence level, 0.05 margin of error, and an unknown population size due to fluctuating admission rates during the conflict. The minimum required sample was 384 records (approximately 64 per hospital). To enhance precision, we targeted 100 records per hospital, yielding a total of 604 records. Data collection and variables Data were collected using a pre-tested checklist developed based on the standard patient record format used in the participating hospitals. The checklist included five domains. The first domain covered sociodemographic data and consisted of seven items: patient name, age, occupation, phone number, residency, admission date, and discharge date. The second domain addressed patient history and included nine items such as chief complaint, history of presenting illness, and past medical history. The third domain focused on management and investigations, comprising two items related to investigations and treatments. The fourth domain assessed operative documentation and contained ten items, including the name of the operation, procedure performed, and type of anaesthesia. Finally, the fifth domain evaluated handwriting clarity through a single item. Each item was scored as “adequately filled”, “inadequately filled”, or “not filled”, and completeness was calculated as the proportion of adequately filled items within each domain. Statistical analysis Data were analysed using SPSS version 26. Descriptive statistics were presented as frequencies and percentages for categorical variables and mean (SD) for continuous variables. One-way ANOVA was used to compare completeness rates across hospitals and departments. A p-value < 0.05 was considered statistically significant. Results A total of 604 medical records were reviewed from six public hospitals: El-Obeid (n = 102), El- Hasahisa (n = 101), Atbara (n = 101), Marawi (n = 100), Kosti (n = 100), and Port Sudan (n = 100). Across departments, General Surgery contributed the largest proportion (n = 302), followed by Orthopaedic Surgery (n = 133), Urology (n = 100), and Neurosurgery (n = 8) (Table 1 ). Table 1 Distribution of the medical records sample among hospitals and departments Department El-Obeid n (%) El-Hasahisa n (%) Marawi n (%) Kosti n (%) Port Sudan n (%) Atbara n (%) Total General surgery 86 (84.3%) 45 (44.6%) 67 (67%) 26 (26%) 38 (38%) 34 (33.6%) 302 Plastic surgery 0 13 (12.9%) 2 (2%) 0 7 (7%) 19 (18.8%) 41 Paediatrics surgery 0 0 21 (21%) 0 5 (5%) 0 26 Urological surgery 3 (2.9%) 9 (8.9%) 10 (10%) 35 (35%) 18 (18%) 25 (24.8%) 100 Orthopaedic surgery 13 (12.7%) 34 (33.7%) 0 39 (39%) 30 (30%) 17 (16.8%) 133 Neurosurgery 0 0 0 0 2 (2%) 6 (6%) 2 Total 102 101 100 100 100 101 604 Socio-demographic data Age, date of admission, and patient name were the most consistently documented fields, present in 98.7%, 96.9%, and 92.9% of records, respectively. Occupation was recorded in 62.4%, while the date of discharge appeared in 51.7%. Phone numbers were least documented (31.3%). Residence was adequately recorded in 33.4%, with 61.6% noted as incomplete. The mean completeness score for socio-demographic data was 66.7% (SD ± 21) (Table 2 ; Fig. 1 ). Table 2 Completeness of Medical Records Across Sociodemographic, Patient History, Patient Management, and Operative Sheet Fields Field Adequately filled n (%) Inadequately filled n (%) Not filled n (%) Sociodemographic data Name 561 (92.9%) - 43 (7.1%) Age 596 (98.7%) - 8 (1.3%) Occupation 377 (62.4%) - 227 (37.6%) Phone Number 189 (31.3%) - 415 (68.7%) Residence 202 (33.4%) 372 (61.6%) 30 (5%) Date of admission 585 (96.9%) - 19 (3.1%) Date of discharge 312 (51.7%) - 292 (48.3%) Sociodemographic fields (Total = 4228) 2822 (66.7%) 372 (8.8%) 1034 (24.5%) Patient history Main complain/s 358 (59.3%) 189 (31.3%) 57 (9.4%) History of presenting complaints 303 (50.2%) 164 (27.1%) 137 (22.7%) Treatment given 281 (46.5%) 93 (15.4%) 230 (38.1%) History of chronic diseases 286 (47.4%) 80 (13.2%) 238 (39.4%) History of admission 200 (33.1%) 75 (12.4%) 329 (54.5%) Past surgical history 282 (46.7%) 44 (7.3%) 278 (46%) Drug history (allergy) 290 (48%) 45 (7.5%) 269 (44.5%) Social history 88 (14.6%) 22 (3.6%) 494 (81.8%) Health insurance 46 (7.6%) - 558 (92.4%) Patient history fields (Total = 5436) 2134 (39.3%) 712 (13.1%) 2590 (47.6%) Patient management Investigations 258 (42.7%) 143 (23.7%) 203 (33.6%) Treatments 410 (67.9%) 100 (16.5%) 94 (15.6%) Patient Management fields (Total = 1208) 668 (55.3%) 243 (20.1%) 297 (24.6%) Patient operative sheet Name of operation 468 (77.5%) - 136 (22.5%) Procedure done 459 (76%) - 145 (24%) Type of anaesthesia 401 (66.4%) - 203 (33.6%) Type of incision 281 (46.5%) - 323 (53.5%) Intraoperative findings 294 (48.7%) 109 (18%) 201 (33.3%) Postoperative care 390 (64.6%) 57 (9.4%) 157 (26%) Name of surgeon 439 (72.7%) - 165 (27.3%) Name of assistant 394 (65.2%) - 210 (34.8%) Name of anaesthetist 328 (54.3%) - 276 (45.7%) Patient operation notes fields (Total = 6040) 3454 (63.5%) 166 (3.1%) 1816 (33.4%) Patient history The main complaint field (main complaint/s and their duration) was properly filled in 59.3% of records, poorly filled in 31.3%, and not filled at all in 9.4%. The history of presenting illness was complete in 50.2%, poorly filled in 27.1%, and absent in 22.7%. Details of treatment given before admission were adequately documented in 47.4% of cases. Past medical history and past surgical history were recorded adequately in 47.4% and 46.7%, respectively. Drug history was documented in 48.0%. Social history and health insurance details were the most frequently omitted fields (81.8% and 92.4%, respectively). The mean completeness score for patient history was 39.2% (SD ± 26), indicating poor documentation overall (Table 2 ; Fig. 1 ). Investigations and management Out of 604 records, investigations were fully documented (including results with dates) in 42.7%, while 23.7% were recorded insufficiently. Regarding treatments, 67.9% were documented properly, 16.5% were documented poorly and 15.6% were not recorded at all. The mean completeness score for this domain was 55.3% (SD ± 38) (Table 2 ; Fig. 1 ). Operation sheet notes The most frequently completed fields were operation name (77.5%), procedure performed (76%), surgeon’s name (72.7%), and type of anaesthesia (66.4%). Intra-operative findings were adequately documented in 48.7% of records, while postoperative care was complete in 64.6%. The mean completeness score for operative notes was 63.6% (SD ± 35) (Table 2 ; Fig. 1 ). Handwriting readability Overall, 60.3% of records were readable, 32.3% were difficult to read, and 7.4% were illegible (completely unreadable) (Table 3 ). Table 3 Overall handwriting readability Overall handwriting readability Readable (%) Hardly readable (%) Not readable (%) Handwriting 364 (60.3%) 195 (32.3%) 45 (7.4%) Comparison across hospitals and departments One-way ANOVA revealed significant differences in completeness scores between hospitals (p < 0.001) and departments (p < 0.05). El Hasahisa Hospital had the highest overall completeness (mean = 72.9%, SD = 10.9), while Marawi Hospital had the lowest (mean = 41.0%, SD = 16.3). Among departments, Neurosurgery achieved the highest completeness (mean = 78.0%, SD = 0.0), followed by Plastic Surgery (mean = 58.8%, SD = 14.4) and General Surgery (mean = 57.0%, SD = 20.6). Orthopaedic Surgery had the lowest completeness (mean = 50.2%, SD = 19.5) (Table 4 ). Discussion The overall completeness of socio-demographic data (66.7%) is comparable to findings from Ethiopia in 2017 (69%) and Egypt in 2020 (74.3%) [ 5 , 6 ]. Despite being relatively better documented than other domains, gaps remain, particularly in recording phone numbers and addresses. These omissions can hinder follow-up and delay communication of results. Furthermore, demographic details such as age, gender, and occupation are clinically relevant as risk factors for certain conditions, and their absence may compromise diagnostic accuracy and continuity of care. The full patient’s name is nonetheless more important to ensure that the medical information is matched to the correct individual and for legal purposes⁴. Patient history was the most poorly documented domain, with only 39.23% of fields adequately completed. This is concerning given its critical role in ensuring continuity of care, supporting clinical decision-making, and safeguarding patient safety. Complete documentation of clinical data—including history, investigations, and treatments—is essential not only for patient care but also for medical research and education, which rely on accurate records to generate evidence and train healthcare professionals [ 2 , 3 ]. Furthermore, medical records serve an important role in public health surveillance, medication management, and regulatory compliance, making them an essential component of healthcare systems [ 2 , 3 ]. Missing information on past medical and surgical history, drug allergies, and social history can lead to suboptimal treatment and increased risk of adverse events. Investigations and management were adequately documented in 55.30% of records, which still indicates a significant gap. Operative notes showed moderate completeness at 63.59%, but key fields such as anaesthetist name and intra-operative findings were frequently omitted. These findings echo previous studies in Sudan and Ethiopia, which also reported gaps in surgical documentation [ 18 – 20 ]. Given the importance of operative records for patient safety and legal defence, stricter enforcement of documentation standards is warranted [ 4 ]. Handwriting legibility remains a significant challenge, with one-third of records difficult to read and 7% completely illegible. Poor handwriting compromises communication among healthcare providers and increases the risk of errors, as highlighted in prior research [ 20 , 21 ]. Implications for practice Improving documentation requires a multifaceted approach. Audit and feedback cycles have been shown to enhance record quality [ 9 , 12 ]. Additional strategies include standardised forms with clear headings, visual aids, and structured templates; addressing workload and time constraints; and transitioning to electronic health records (EHRs), which have demonstrated benefits in improving completeness and accessibility [ 18 – 22 ]. Strengths and limitations This study is the first multi-centre investigation in Sudan to assess the completeness and readability of medical records across six public hospitals located in different states. Also, including hospitals from various regions provides a broader and more representative perspective than previous studies, which were limited to Khartoum. The use of a large sample size (n = 604), which exceeds the minimum calculated requirement, further strengthens the reliability and generalisability of the findings. Additionally, a standardised, pre-tested checklist was employed to ensure consistency in data collection across all participating hospitals. However, the study also has several limitations. It was conducted during a period of armed conflict, which may have influenced documentation practices due to resource constraints and staff shortages. The scope was limited to public hospitals, with private hospitals and facilities in Khartoum and Darfur excluded, which may affect the generalisability of the results to all healthcare settings in Sudan. Furthermore, completeness was assessed using a simplified definition based on the presence or absence of fields, which does not capture the accuracy or clinical relevance of the documented information. Conclusion Significant deficiencies exist in the documentation of patient information across all domains, compounded by issues of handwriting legibility. These gaps pose risks to patient safety, continuity of care, and medico-legal protection. Targeted interventions—including audits, standardised templates, and adoption of electronic health records—are urgently needed to improve documentation quality and, ultimately, healthcare outcomes in Sudan. Abbreviations SD Standard Deviation EHRs Electronic Health Records SPSS Statistical Package for the Social Sciences ANOVA Analysis of Variance Declarations Authors’ contributions TAAM, MEA contributed to the Conceptualization; MEA, TAAM were involved in the Methodology; MS, AE were involved in data Curation and formal analysis; AE was involved in visualization; MIMA, AAMA, FA, RBA, MJMS, IAAA, TAAM, MO, MM, AE contributed to investigation and Data Collection; TAAM was involved in resources collection; AE , MS , TAAM , MEA, MM, AA wrote the original draft; MEA, MAMOI supervised the study; TAAM, MEA, MO, AE, MM administered the study. All authors revised, edited and approved the last version . Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Availability of data and materials The datasets generated and analysed in this study are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki. Ethical approval was obtained from the ethics review board at the University of Khartoum and Atbara Teaching Hospital. Approval was also obtained from each of the remaining hospitals in this study. The data was used only for research purposes, and the confidentiality of the patient's records was maintained. As the data was collected from records, informed consent from patients had been waived by the ethical review board. Consent to publication Not applicable. Competing interests The authors have no relevant financial or non-financial interests to disclose. References World Health Organization, Regional Office for the Western Pacific. Improving data quality: a guide for developing countries [Internet]. Manila: WHO Regional Office for the Western Pacific. 2003 [cited 2024 Jun 15]. 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Roukema J, Los RK, Bleeker SE, van Ginneken AM, van der Lei J, Moll HA. Paper versus computer: feasibility of an electronic medical record in general pediatrics. Pediatrics. 2006;117(1):15–21. Weiskopf NG, Hripcsak G, Swaminathan S, Weng C. Defining and measuring completeness of electronic health records for secondary use. J Biomed Inf. 2013;46(5):830–6. Table 4 Table 4 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table4.docx 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. 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Osman","lastName":"Idris","suffix":""},{"id":567007157,"identity":"2625b076-e12f-4ace-a312-411b5a87a14e","order_by":16,"name":"Mohamed Elmakki Ahmed","email":"","orcid":"","institution":"University of Khartoum","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Elmakki","lastName":"Ahmed","suffix":""}],"badges":[],"createdAt":"2025-12-16 01:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8370650/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8370650/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99291938,"identity":"d286e145-1a85-4591-80c7-200e410fee05","added_by":"auto","created_at":"2025-12-31 10:39:23","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":53728,"visible":true,"origin":"","legend":"","description":"","filename":"MedicalrecordsSudanBMChealthservicesmanuscriptV1docx.docx","url":"https://assets-eu.researchsquare.com/files/rs-8370650/v1/4b7ad6c62898cfc7f9359667.docx"},{"id":99291945,"identity":"2fbfc864-137f-4800-8a79-d6f68c1905ae","added_by":"auto","created_at":"2025-12-31 10:39:25","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16092,"visible":true,"origin":"","legend":"","description":"","filename":"310d2c5ca6374d3e932330334ddf015a.json","url":"https://assets-eu.researchsquare.com/files/rs-8370650/v1/fe9409f277eeb5221330091f.json"},{"id":99291937,"identity":"689934f1-24c6-426b-b74d-acd25e01b156","added_by":"auto","created_at":"2025-12-31 10:39:23","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87742,"visible":true,"origin":"","legend":"","description":"","filename":"310d2c5ca6374d3e932330334ddf015a1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8370650/v1/a7f1a5c1c1a603bfe40a5c12.xml"},{"id":99291856,"identity":"05e74678-7af0-4717-ab88-371c8a8bcc05","added_by":"auto","created_at":"2025-12-31 10:39:19","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86635,"visible":true,"origin":"","legend":"","description":"","filename":"310d2c5ca6374d3e932330334ddf015a1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8370650/v1/8f81029b81e01a71f3d71634.xml"},{"id":99291941,"identity":"82dc52c0-5291-4e27-be8d-aa2fefbe7e83","added_by":"auto","created_at":"2025-12-31 10:39:23","extension":"html","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93092,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8370650/v1/ad5676b7e2f806d781c14bc4.html"},{"id":99291909,"identity":"cb1ae3b0-60e5-4beb-a521-204f491c419c","added_by":"auto","created_at":"2025-12-31 10:39:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33505,"visible":true,"origin":"","legend":"\u003cp\u003eOverall completeness for each domain and the overall well completeness rate.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8370650/v1/48ff26467a80f0401b6a381a.png"},{"id":104397808,"identity":"70453f58-3caa-447f-b082-b7ff0a1d040d","added_by":"auto","created_at":"2026-03-11 11:56:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1004676,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8370650/v1/dc5c498e-d8a2-4afa-8889-3a41b8cc3f5e.pdf"},{"id":99321108,"identity":"39c1aa21-5e02-4ecf-8d39-a049679f73ad","added_by":"auto","created_at":"2025-12-31 16:39:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18399,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8370650/v1/00b6065abe6cf1c4f952a35f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quality of Medical Records in Sudanese Public Hospitals During Armed Conflict: A Multi-Centre Cross-Sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eA medical record is a comprehensive account of a patient\u0026rsquo;s health history, including past and present illnesses and treatments, documented by healthcare professionals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Accurate medical record-keeping is essential for delivering high-quality patient care, preventing delays and complications, and supporting medical research and education [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, well-maintained records serve as a primary defence for healthcare professionals against malpractice claims [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these critical roles, numerous studies have reported deficiencies in medical record documentation worldwide, including in Sudan [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Contributing factors include heavy workloads, time constraints, involvement of multiple care providers, limited knowledge and experience, and, importantly, the perception that documentation is not an integral part of treatment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Strategies such as audits with feedback, reminders, templates, and multifaceted interventions have been proposed to address these gaps [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence from countries such as China and Brazil indicates frequent omissions in medical records, including incomplete documentation of symptoms, non-drug treatments, and diagnoses [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, a retrospective study in Ethiopia found that only 18.4% of medical records met national standards, highlighting significant deficiencies in completeness and legibility, particularly in health centres compared to hospitals [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Sudan, many discharge summaries fail to follow the structured, predesigned format, resulting in inadequate and inconsistent health information [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, studies have revealed insufficient knowledge and poor documentation practices among nurses, underscoring the need for in-service training [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the importance of medical record systems is increasingly recognised, most Sudanese studies have been small-scale and limited to Khartoum State, failing to reflect the national situation. This study aims to assess the quality of medical records\u0026mdash;specifically their completeness and readability\u0026mdash;across multiple states and to compare documentation quality between hospitals and departments. The findings will provide a foundation for improving medical record practices in Sudanese hospitals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional, multi-centre study in October 2023 to assess the quality of medical records in surgical departments across six public hospitals in Sudan: Marawi, Atbara, Port Sudan, El-Obeid, El-Hasahisa, and Kosti Teaching Hospitals. These hospitals are in major cities representing different states (North, East, Central, and South). Selection was based on accessibility and safety during the ongoing armed conflict.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and eligibility criteria\u003c/h3\u003e\n\u003cp\u003eMedical records were obtained from the statistics departments of the selected hospitals. Inclusion criteria were records of patients admitted to surgical wards who underwent surgical procedures during October 2023. Records of patients admitted but not operated on were excluded.\u003c/p\u003e\n\u003ch3\u003eSample size and sampling\u003c/h3\u003e\n\u003cp\u003eThe sample size was calculated using Epi Info\u0026trade; for population surveys, assuming a 95% confidence level, 0.05 margin of error, and an unknown population size due to fluctuating admission rates during the conflict. The minimum required sample was 384 records (approximately 64 per hospital). To enhance precision, we targeted 100 records per hospital, yielding a total of 604 records.\u003c/p\u003e\n\u003ch3\u003eData collection and variables\u003c/h3\u003e\n\u003cp\u003eData were collected using a pre-tested checklist developed based on the standard patient record format used in the participating hospitals. The checklist included five domains. The first domain covered sociodemographic data and consisted of seven items: patient name, age, occupation, phone number, residency, admission date, and discharge date. The second domain addressed patient history and included nine items such as chief complaint, history of presenting illness, and past medical history. The third domain focused on management and investigations, comprising two items related to investigations and treatments. The fourth domain assessed operative documentation and contained ten items, including the name of the operation, procedure performed, and type of anaesthesia. Finally, the fifth domain evaluated handwriting clarity through a single item. Each item was scored as \u0026ldquo;adequately filled\u0026rdquo;, \u0026ldquo;inadequately filled\u0026rdquo;, or \u0026ldquo;not filled\u0026rdquo;, and completeness was calculated as the proportion of adequately filled items within each domain.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were analysed using SPSS version 26. Descriptive statistics were presented as frequencies and percentages for categorical variables and mean (SD) for continuous variables. One-way ANOVA was used to compare completeness rates across hospitals and departments. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 604 medical records were reviewed from six public hospitals: El-Obeid (n\u0026thinsp;=\u0026thinsp;102), El- Hasahisa (n\u0026thinsp;=\u0026thinsp;101), Atbara (n\u0026thinsp;=\u0026thinsp;101), Marawi (n\u0026thinsp;=\u0026thinsp;100), Kosti (n\u0026thinsp;=\u0026thinsp;100), and Port Sudan (n\u0026thinsp;=\u0026thinsp;100). Across departments, General Surgery contributed the largest proportion (n\u0026thinsp;=\u0026thinsp;302), followed by Orthopaedic Surgery (n\u0026thinsp;=\u0026thinsp;133), Urology (n\u0026thinsp;=\u0026thinsp;100), and Neurosurgery (n\u0026thinsp;=\u0026thinsp;8) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of the medical records sample among hospitals and departments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepartment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEl-Obeid\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEl-Hasahisa\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarawi\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKosti\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePort Sudan\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAtbara\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (84.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (44.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlastic surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaediatrics surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrological surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrthopaedic surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (33.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17 (16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurosurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSocio-demographic data\u003c/h3\u003e\n\u003cp\u003eAge, date of admission, and patient name were the most consistently documented fields, present in 98.7%, 96.9%, and 92.9% of records, respectively. Occupation was recorded in 62.4%, while the date of discharge appeared in 51.7%. Phone numbers were least documented (31.3%). Residence was adequately recorded in 33.4%, with 61.6% noted as incomplete. The mean completeness score for socio-demographic data was 66.7% (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;21) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCompleteness of Medical Records Across Sociodemographic, Patient History, Patient Management, and Operative Sheet Fields\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdequately filled\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInadequately filled n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot filled\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographic data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e561 (92.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e596 (98.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377 (62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhone Number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e415 (68.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (33.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e372 (61.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDate of admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e585 (96.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDate of discharge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312 (51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e292 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographic fields (Total\u0026thinsp;=\u0026thinsp;4228)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2822 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e372 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1034 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain complain/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e358 (59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of presenting complaints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e303 (50.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164 (27.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment given\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281 (46.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 (33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e329 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast surgical history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e278 (46%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug history (allergy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e269 (44.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e494 (81.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e558 (92.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient history fields\u003c/p\u003e \u003cp\u003e(Total\u0026thinsp;=\u0026thinsp;5436)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2134 (39.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e712 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2590 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestigations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258 (42.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e203 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e410 (67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient Management fields (Total\u0026thinsp;=\u0026thinsp;1208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e668 (55.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e243 (20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e297 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient operative sheet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName of operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e468 (77.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcedure done\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e459 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145 (24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of anaesthesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e401 (66.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e203 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of incision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281 (46.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e294 (48.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e390 (64.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157 (26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName of surgeon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e439 (72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName of assistant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e394 (65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName of anaesthetist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e276 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient operation notes fields (Total\u0026thinsp;=\u0026thinsp;6040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3454 (63.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1816 (33.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePatient history\u003c/h3\u003e\n\u003cp\u003eThe main complaint field (main complaint/s and their duration) was properly filled in 59.3% of records, poorly filled in 31.3%, and not filled at all in 9.4%. The history of presenting illness was complete in 50.2%, poorly filled in 27.1%, and absent in 22.7%. Details of treatment given before admission were adequately documented in 47.4% of cases.\u003c/p\u003e \u003cp\u003ePast medical history and past surgical history were recorded adequately in 47.4% and 46.7%, respectively. Drug history was documented in 48.0%. Social history and health insurance details were the most frequently omitted fields (81.8% and 92.4%, respectively). The mean completeness score for patient history was 39.2% (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;26), indicating poor documentation overall (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInvestigations and management\u003c/h2\u003e \u003cp\u003eOut of 604 records, investigations were fully documented (including results with dates) in 42.7%, while 23.7% were recorded insufficiently. Regarding treatments, 67.9% were documented properly, 16.5% were documented poorly and 15.6% were not recorded at all. The mean completeness score for this domain was 55.3% (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;38) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eOperation sheet notes\u003c/h2\u003e \u003cp\u003eThe most frequently completed fields were operation name (77.5%), procedure performed (76%), surgeon\u0026rsquo;s name (72.7%), and type of anaesthesia (66.4%). Intra-operative findings were adequately documented in 48.7% of records, while postoperative care was complete in 64.6%. The mean completeness score for operative notes was 63.6% (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;35) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHandwriting readability\u003c/h2\u003e \u003cp\u003eOverall, 60.3% of records were readable, 32.3% were difficult to read, and 7.4% were illegible (completely unreadable) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall handwriting readability\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall handwriting readability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReadable (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHardly readable (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot readable (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHandwriting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e364 (60.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison across hospitals and departments\u003c/h2\u003e \u003cp\u003eOne-way ANOVA revealed significant differences in completeness scores between hospitals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and departments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). El Hasahisa Hospital had the highest overall completeness (mean\u0026thinsp;=\u0026thinsp;72.9%, SD\u0026thinsp;=\u0026thinsp;10.9), while Marawi Hospital had the lowest (mean\u0026thinsp;=\u0026thinsp;41.0%, SD\u0026thinsp;=\u0026thinsp;16.3). Among departments, Neurosurgery achieved the highest completeness (mean\u0026thinsp;=\u0026thinsp;78.0%, SD\u0026thinsp;=\u0026thinsp;0.0), followed by Plastic Surgery (mean\u0026thinsp;=\u0026thinsp;58.8%, SD\u0026thinsp;=\u0026thinsp;14.4) and General Surgery (mean\u0026thinsp;=\u0026thinsp;57.0%, SD\u0026thinsp;=\u0026thinsp;20.6). Orthopaedic Surgery had the lowest completeness (mean\u0026thinsp;=\u0026thinsp;50.2%, SD\u0026thinsp;=\u0026thinsp;19.5) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThe overall completeness of socio-demographic data (66.7%) is comparable to findings from Ethiopia in 2017 (69%) and Egypt in 2020 (74.3%) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite being relatively better documented than other domains, gaps remain, particularly in recording phone numbers and addresses. These omissions can hinder follow-up and delay communication of results. Furthermore, demographic details such as age, gender, and occupation are clinically relevant as risk factors for certain conditions, and their absence may compromise diagnostic accuracy and continuity of care. The full patient\u0026rsquo;s name is nonetheless more important to ensure that the medical information is matched to the correct individual and for legal purposes⁴. Patient history was the most poorly documented domain, with only 39.23% of fields adequately completed.\u003c/p\u003e \u003cp\u003eThis is concerning given its critical role in ensuring continuity of care, supporting clinical decision-making, and safeguarding patient safety. Complete documentation of clinical data\u0026mdash;including history, investigations, and treatments\u0026mdash;is essential not only for patient care but also for medical research and education, which rely on accurate records to generate evidence and train healthcare professionals [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, medical records serve an important role in public health surveillance, medication management, and regulatory compliance, making them an essential component of healthcare systems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Missing information on past medical and surgical history, drug allergies, and social history can lead to suboptimal treatment and increased risk of adverse events.\u003c/p\u003e \u003cp\u003eInvestigations and management were adequately documented in 55.30% of records, which still indicates a significant gap. Operative notes showed moderate completeness at 63.59%, but key fields such as anaesthetist name and intra-operative findings were frequently omitted. These findings echo previous studies in Sudan and Ethiopia, which also reported gaps in surgical documentation [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Given the importance of operative records for patient safety and legal defence, stricter enforcement of documentation standards is warranted [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHandwriting legibility remains a significant challenge, with one-third of records difficult to read and 7% completely illegible. Poor handwriting compromises communication among healthcare providers and increases the risk of errors, as highlighted in prior research [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImplications for practice\u003c/h2\u003e \u003cp\u003eImproving documentation requires a multifaceted approach. Audit and feedback cycles have been shown to enhance record quality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additional strategies include standardised forms with clear headings, visual aids, and structured templates; addressing workload and time constraints; and transitioning to electronic health records (EHRs), which have demonstrated benefits in improving completeness and accessibility [\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study is the first multi-centre investigation in Sudan to assess the completeness and readability of medical records across six public hospitals located in different states. Also, including hospitals from various regions provides a broader and more representative perspective than previous studies, which were limited to Khartoum. The use of a large sample size (n\u0026thinsp;=\u0026thinsp;604), which exceeds the minimum calculated requirement, further strengthens the reliability and generalisability of the findings. Additionally, a standardised, pre-tested checklist was employed to ensure consistency in data collection across all participating hospitals.\u003c/p\u003e \u003cp\u003eHowever, the study also has several limitations. It was conducted during a period of armed conflict, which may have influenced documentation practices due to resource constraints and staff shortages. The scope was limited to public hospitals, with private hospitals and facilities in Khartoum and Darfur excluded, which may affect the generalisability of the results to all healthcare settings in Sudan. Furthermore, completeness was assessed using a simplified definition based on the presence or absence of fields, which does not capture the accuracy or clinical relevance of the documented information.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSignificant deficiencies exist in the documentation of patient information across all domains, compounded by issues of handwriting legibility. These gaps pose risks to patient safety, continuity of care, and medico-legal protection. Targeted interventions\u0026mdash;including audits, standardised templates, and adoption of electronic health records\u0026mdash;are urgently needed to improve documentation quality and, ultimately, healthcare outcomes in Sudan.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEHRs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic Health Records\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSPSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eANOVA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of Variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003eTAAM, MEA contributed to the Conceptualization; MEA, TAAM were involved in the Methodology; MS, AE were involved in data Curation and formal analysis; AE was involved in visualization; MIMA, AAMA, FA, RBA, MJMS, IAAA, TAAM, MO, MM, AE contributed to investigation and Data Collection; TAAM was involved in resources collection; AE\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eMS\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eTAAM\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eMEA, MM, AA wrote the original draft; MEA, MAMOI supervised the study; TAAM, MEA, MO, AE, MM administered the study. All authors revised, edited and approved the last version\u003cstrong\u003e.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003eThe datasets generated and analysed in this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Ethical approval was obtained from the ethics review board at the University of Khartoum and Atbara Teaching Hospital. Approval was also obtained from each of the remaining hospitals in this study. The data was used only for research purposes, and the confidentiality of the patient\u0026apos;s records was maintained. As the data was collected from records, informed consent from patients had been waived by the ethical review board. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization, Regional Office for the Western Pacific. Improving data quality: a guide for developing countries [Internet]. Manila: WHO Regional Office for the Western Pacific. 2003 [cited 2024 Jun 15]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/handle/10665/206974\u003c/span\u003e\u003cspan address=\"https://iris.who.int/handle/10665/206974\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNgo E, Patel N, Chandrasekaran K, Tajik J, Paterick TE. The importance of the medical record: a critical professional responsibility. J Med Pract Manage. 2016;31:305\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhani Y, Thakrar R, Kosuge D, Bates P. Smart\u0026rsquo; electronic operation notes in surgery: an innovative way to improve patient care. Int J Surg. 2014;12(1):30\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMozaffarieh M, Wedrich A. Malpractice in ophthalmology: guidelines for preventing pitfalls. Med Law. 2006;25:257\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammed Z, Arafa A, Senosy S, El-Morsy EMA, El-Bana E, Saleh Y, et al. Completeness of medical records of trauma patients admitted to the emergency unit of a university hospital, Upper Egypt. Int J Environ Res Public Health. 2021;18(1):83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEndriyas M, Kawza A, Alano A, Lemango F. Quality of medical records in public health facilities: a case of Southern Ethiopia, resource limited setting. Health Inf J. 2022;28(3):14604582221112853.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsghari Z, Mardanshahi A, Farahabadi EB, Siamian H, Gorji AMH, Saravi BM, et al. The quantitative study of the faculty members performance in documentation of the medical records in teaching hospitals of Mazandaran University of Medical Sciences. Mater Sociomed. 2016;28(4):292\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChichom-Mefire A, Nwanna-Nzewunwa OC, Siysi VV, Feldhaus I, Dicker R, Juillard C. Key findings from a prospective trauma registry at a regional hospital in Southwest Cameroon. PLoS ONE. 2017;12(7):e0180784.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGasoma EBY. Retrospective audit in documentation practice in surgical inpatients records, a two cycles audit. Ir J Med Sci. 2023;192(5):2345\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoss S, Ryan C, Duncan EM, Francis JJ, Johnston M, Ker JS, et al. Perceived causes of prescribing errors by junior doctors in hospital inpatients: a study from the PROTECT programme. BMJ Qual Saf. 2013;22(2):97\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaravi BM, Asgari Z, Siamian H, Farahabadi EB, Gorji AH, Motamed N, et al. Documentation of medical records in hospitals of Mazandaran University of Medical Sciences in 2014: a quantitative study. Acta Inf Med. 2016;24(3):202\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLorenzetti DL, Quan H, Lucyk K, Cunningham C, Hennessy D, Jiang J, et al. Strategies for improving physician documentation in the emergency department: a systematic review. BMC Emerg Med. 2018;18(1):36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Zhou H, Ma X, Shi Y, Xue H, Zhou C, et al. Using standardised patients to assess the quality of medical records: an application and evidence from rural China. BMJ Qual Saf. 2020;29(6):491\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRibeiro MC, Dalaneze BS, De Oliveira Peruchi MP, Cintra RB. Analysis of medical records of a university hospital in Mogi das Cruzes, S\u0026atilde;o Paulo, Brazil. Rev Bioet. 2020;28(4):740\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDafaalla M, Abdalrahman I, Kheir A, Nimir M. Adequacy of the discharge summary at Soba University Hospital: where are we now? J Clin Audits. 2014;6:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEissa AYH, Mohamed Elhassan AZW, Ahmed AZH, Elgadi A, Manhal GAA, Fadul MH et al. The quality of discharge summaries at Al-Shaab Hospital, Sudan, in 2022: the first cycle of a clinical audit. Cureus [Internet]. 2023 [cited 2024 Jun 5]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cureus.com/articles/148817-the-quality-of-discharge-summaries-at-al-shaab-hospital-sudan-in-2022-the-first-cycle-of-a-clinical-audit\u003c/span\u003e\u003cspan address=\"https://www.cureus.com/articles/148817-the-quality-of-discharge-summaries-at-al-shaab-hospital-sudan-in-2022-the-first-cycle-of-a-clinical-audit\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFadl Elmula ZS, Bolad AK. Standard documentation of paper-based medical records at four main hospitals in Khartoum State, Sudan, 2014\u0026ndash;2015. Al-Basar Int J Ophthalmol. 2017;4(3):75\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamza AA, Abdalrahim HM, Idris SA, Ahmed OM. Evaluating the operative notes of patients undergoing surgery at Omdurman Teaching Hospital, Sudan. Ann Med Surg. 2024;86(1):92\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTegegne NG, Fentie DY, Tegegne BA, Admassie BM. Assessment of manual operation note documentation practice: a cross-sectional study. Ann Med Surg. 2024;86(1):92\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLefter LP, Walker SR, Dewhurst F, Turner RWL. An audit of operative notes: facts and ways to improve. ANZ J Surg. 2008;78(9):800\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoukema J, Los RK, Bleeker SE, van Ginneken AM, van der Lei J, Moll HA. Paper versus computer: feasibility of an electronic medical record in general pediatrics. Pediatrics. 2006;117(1):15\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiskopf NG, Hripcsak G, Swaminathan S, Weng C. Defining and measuring completeness of electronic health records for secondary use. J Biomed Inf. 2013;46(5):830\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 4","content":"\u003cp\u003eTable 4 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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 records, Documentation quality, Completeness, Readability, Public hospitals, Sudan, Surgery, Patient safety, Health information management","lastPublishedDoi":"10.21203/rs.3.rs-8370650/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8370650/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eAccurate and complete medical records are essential for effective health service delivery, patient safety, and quality improvement. However, deficiencies in documentation remain a challenge in many low-resource settings. This study aimed to assess the completeness and readability of medical records in public hospitals across Sudan, providing evidence to inform health system strengthening.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted in October 2023, reviewing 604 medical records from surgical departments in six public hospitals across Sudan. Records were evaluated for completeness using a standardised checklist covering five domains: socio-demographic data, patient history, investigations and management, operation sheet notes, and handwriting clarity. Descriptive statistics were used to summarise findings, and one-way ANOVA was applied to compare completeness rates between hospitals and departments.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe overall completeness rate of medical records was 55.68% (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;20). Socio-demographic data were 66.7% complete, patient history 39.23%, investigations and management 55.30%, and operation sheet notes 63.59%. Handwriting was readable in 60.3% of records. Significant differences in completeness were observed between hospitals and departments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eSubstantial deficiencies in documentation and legibility were identified in Sudanese public hospitals, with notable variation across institutions and departments. Targeted interventions, such as standardised templates and electronic health records, are needed to improve documentation quality and support better health service delivery.\u003c/p\u003e","manuscriptTitle":"Quality of Medical Records in Sudanese Public Hospitals During Armed Conflict: A Multi-Centre Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 10:38:54","doi":"10.21203/rs.3.rs-8370650/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":"0b087361-3d85-4a55-97cb-1e2e615f53d5","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T06:57:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-31 10:38:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8370650","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8370650","identity":"rs-8370650","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}