From Register to Electronic Medical Records: A Data-Quality Audit in a Bangladeshi Orthopaedic Outpatient Department

From Register to Electronic Medical Records: A Data-Quality Audit in a Bangladeshi Orthopaedic Outpatient Department | 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 From Register to Electronic Medical Records: A Data-Quality Audit in a Bangladeshi Orthopaedic Outpatient Department Md Ebrahim Molla, Md Nazrul Islam, Rajib Kumar Paul, Md Mohsin Ali Farazi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7791604/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Electronic medical records (EMRs) are promoted to improve data quality, yet most hospitals in low- and middle-income countries (LMICs) still rely on paper or semi-electronic registers. Orthopaedic outpatient departments face unique documentation challenges due to complex coding, high caseloads, and limited digital integration. Evidence from empirical audits that quantify register deficiencies and map them to EMR safeguards remains sparse in Bangladesh and comparable LMIC settings. Methods We conducted a retrospective audit of all orthopaedic outpatient records (N = 4,085) at Khulna Medical College Hospital, Bangladesh, from 1 January to 30 June 2025. Three dimensions of data quality were assessed: completeness, validity (format, plausibility, categorical conformance), and internal consistency (duplicate detection, age–age group concordance). Missingness and violation rates were quantified with 95% Wilson confidence intervals. Identified deficiencies were translated into EMR configuration recommendations, including mandatory fields, constrained inputs, ICD-10 picklists, and database-enforced unique identifiers. Results Core demographic fields demonstrated high completeness: sex 98.6%, numeric age 98.7%, and occupation 94.9%. Presenting complaint, anatomical site, and painkiller history exceeded 96% completeness. Validity was strong, with no implausible ages (0–120 years) and only 0.5% unparsable dates. Identifier integrity was the principal weakness: 131 duplicate serial numbers (3.2%) and seven complete-row duplicates were detected. Age–age group mismatches were rare (0.2%). Conclusion Orthopaedic outpatient registers in this high-volume tertiary hospital are broadly EMR-ready, with most fields accurate and complete. Key vulnerabilities—duplicate identifiers and free-text complaints—can be addressed by unique visit IDs, constrained input controls, and ICD-10–aligned coding. Findings provide a practical blueprint for EMR configuration in resource-constrained settings, offering immediate pathways to safer care, credible service metrics, and scalable digital health analytics. Data quality audit Electronic medical records Orthopaedics Health informatics Low- and middle-income countries Bangladesh Figures Figure 1 Introduction Musculoskeletal conditions account for a large share of outpatient burden in tertiary hospitals across low- and middle-income countries, where orthopaedic clinics typically manage high caseloads under resource constraints [ 1 ], [ 2 ]. In such settings, routine clinical records—whether handwritten registers or semi-structured forms—constitute the backbone for patient care, quality monitoring, and strategic planning [ 3 ], [ 4 ]. The utility of these records depends critically on data quality: fields must be complete, valid, and internally consistent to support safe and reliable decision making [ 5 ], [ 6 ], [ 7 ]. Electronic medical records (EMRs) are widely promoted to enhance data quality by enforcing structured data entry, real-time validation, and standardised terminologies[ 8 ], [ 9 ]. An emerging literature describes how EMR systems can reduce missingness, flag implausible values, and standardise coding, thus reducing transcription error and supporting high-quality clinical analytics [ 10 ], [ 11 ]. For example, recent reviews emphasise that automation of data quality assessment (DQA) is gaining traction but remains fragmented and context-dependent [ 12 ]. In health registries and clinical data systems, frameworks for data quality (completeness, value conformance, consistency, plausibility) guide systematic auditing and remediation[ 13 ], [ 14 ]. Across clinical specialties in LMICs, audits consistently show variation in data completeness and conformance. A hospital-wide review in Pakistan found uneven completeness across departments, motivating tailored forms and validation rules [ 15 ]. In Ethiopia, emergency-department triage datasets exhibited distinct completeness profiles versus inpatient records, reinforcing the need for specialty-specific data-quality checks [ 16 ]. Recent methodological reviews of EMR data-quality assessment reinforce these findings, recommending domain-specific rules for completeness, value conformance, and plausibility rather than one-size-fits-all tools [ 17 ]. Orthopaedic records add unique challenges: clinical decisions often depend on integration with imaging systems (PACS), yet limited connectivity in LMICs can fragment records[ 18 ], [ 19 ]. In addition, orthopaedic diagnosis and procedure coding are complex, with high potential for ambiguity if structured templates are absent [ 20 ]. Trauma and surgical registries in resource-constrained settings also highlight frequent identifier errors and incomplete operative details, strengthening the case for structured data capture at the point of care [ 21 ], [ 22 ]. Despite national digital health initiatives in Bangladesh, many health departments continue to rely on paper-based or semi-electronic registers with heterogeneous field definitions and free-text entries due to a lack of a robust digital infrastructure [ 23 ]. WHO’s review of digital health in Bangladesh notes that digital registry adoption remains uneven despite policy support [ 24 ]. Few empirical audits in hospital settings in LMICs exist that quantify register deficiencies and map those deficiencies to practical EMR safeguards [ 25 ], [ 26 ]. This gap both limits confidence in the secondary use of legacy data and complicates the design of EMR forms that are resilient to real-world workflow constraints. Similar challenges in register standardisation have been documented in Bangladesh’s newborn and inpatient registries, motivating national efforts toward standard registers [ 27 ]. To fill this gap, we conducted a structured data-quality audit of the Orthopaedic Outpatient Department (OPD) at Khulna Medical College Hospital (KMCH), a large public tertiary centre. Drawing from the complete OPD register export, we assessed three key dimensions of data quality—completeness, validity (format, value plausibility, and categorical constraints), and internal consistency (duplicate detection, age–age-group concordance). We then translated observed deficiencies into a concise set of EMR configuration rules: mandatory core fields, bounded date pickers, numeric age with auto-derived age group, ICD-10 picklists, and database-enforced unique visit identifiers. The audit was designed for maximal translatability: we report field-level missingness and violation rates with 95% confidence intervals, but do not modify raw data, thus preserving audit objectivity. Our Results show excellent capture of core demographics, a few unparsable dates, zero out-of-range age values, but notable duplication among visit identifiers. These findings not only establish a baseline but also offer a direct blueprint for EMR form and rule design in busy orthopaedic OPDs. Our study aimed to quantify completeness, validity, and internal consistency of core fields in the KMCH orthopaedic OPD register and translate audit findings into a prioritised set of EMR validation and configuration rules suitable for routine deployment. Methods Study Design and Setting We conducted a retrospective data quality audit of routine outpatient visit records from the Orthopaedic Outpatient Department (OPD) of Khulna Medical College Hospital (KMCH), Khulna, Bangladesh. KMCH is a 500-bed tertiary teaching hospital serving Khulna city and surrounding districts, which has to provide healthcare services far beyond its designated capacity. The audit encompassed all available records from the OPD register export, covering the period from 1 January 2025 to 31 June 2025. The primary objective was to quantify the completeness, validity, and internal consistency of core data elements and translate identified quality gaps into implementable electronic medical record (EMR) validation rules suitable for a tertiary care orthopaedic setting. Data Source and Variables The OPD register was maintained in a structured paper logbook during routine care and transcribed monthly by trained hospital staff into a hospital-managed Excel file (“Data.xlsx”). No EMR system was in routine use at KMCH during the audit period. For this audit, we used the full exported dataset. Prior to analysis, we performed random 5% spot checks against the original paper register to verify transcription accuracy. In addition, internal logic rules (date plausibility, numeric age range, and age–age group concordance) were applied as quality-control checks. The analysis dataset was de-identified before audit, and all transformations were performed on analysis copies to preserve the source file. Data Pre-processing Several pre-processing steps were performed to ensure data integrity: (i) value normalization, with sex recoded to standardized categories (Male/Female/Other) and illegible entries treated as invalid; (ii) permissive date parsing using multiple common formats; (iii) numeric age coercion with range validation; and (iv) duplicate identification without removal. Duplicate records were flagged at two levels: complete row duplicates (identical across all fields) and serial number duplicates (regardless of other field values) to detect identifier collisions. Age group codes were retained as categorical labels since code-to-band mapping documentation was unavailable. Data Quality Assessment Framework We evaluated three established data quality dimensions following international healthcare data quality standards. Completeness Assessment Completeness was quantified as the proportion of records containing valid, non-missing content for each variable. Missing data included empty fields, null values, unparsable entries, and non-standard categorical values. Validity Assessment Format and value validity were assessed using pre-specified clinical and logical rules: Temporal validity Visit dates before 1 January 2020 or after the data collection endpoint were flagged as implausible Demographic validity Numeric age outside the 0-120 year range was considered invalid Categorical validity Sex values outside the standard {Male, Female, Other} categories were flagged Data format validity Unparsable date formats were enumerated Consistency Assessment Internal consistency evaluation focused on identifier logic, specifically duplicate serial number detection. We also performed age–age group concordance checks, comparing numeric age values against categorical group assignments to detect mismatches. Presenting complaints were analyzed using exploratory keyword screening to demonstrate potential EMR age-appropriate clinical decision support implementation. Statistical Analysis Continuous variables were summarized using mean, standard deviation, median, interquartile range, and 5th and 95th percentiles. Data quality metrics were reported as proportions with 95% confidence intervals calculated using the Wilson score method, which provides superior coverage for extreme probabilities compared to normal approximation methods. No hypothesis testing was performed as the study objective was descriptive estimation rather than statistical inference. Implementation-Focused Analysis Each identified data quality issue was systematically mapped to specific EMR configuration recommendations: mandatory field validation with save-prevention for core data elements, constrained input controls (bounded date pickers, numeric ranges), standardised picklists for sex and presenting complaints aligned with ICD-10 classifications, automatic derivations (age group from numeric age), and database-level uniqueness constraints for visit identifiers. The robustness of date parsing was evaluated using multiple format recognition strategies. Duplicate detection employed both complete-record and identifier-based definitions to ensure comprehensive coverage. All analyses were performed using Python (pandas, NumPy, SciPy libraries), with Wilson confidence intervals computed via scipy.stats.binomtest (...).proportion_ci (method='wilson') Ethical Considerations This retrospective audit of de-identified administrative data was conducted in accordance with institutional guidelines for quality improvement activities. The study focused on data quality assessment rather than patient-level analysis, with all findings reported in aggregate form to protect patient confidentiality. Ethical clearance for the study was granted by the Institutional Ethical Review Board of Khulna Medical College, Bangladesh (Reference No.: KMC/ERC/15; approval date: 6th August 2025). Result Demographic snapshot Sex was recorded as female in 2,066 (50.6%) and male in 1,962 (48.0%), with 57 entries missing/invalid (1.4%; 95% CI 1.1–1.8). Numeric age was available for 4,031/4,085 visits (98.7%; 95% CI 98.3–99.0); 54/4,085 were missing (1.3%; 95% CI 1.0–1.7). The age distribution had median of 40 years (IQR 27–52), a mean of 39.4 (SD 17.6), and a range of 0–100. Occupation was recorded for most patients, with homemakers 1623 (39.7%), students 742 (18.2%), and labourers 731 (17.9%) predominating; 207 (5.1%) of occupation fields were missing (95% CI 4.4–5.8). The overall demographic distributions of sex, age, and occupation are shown in Fig. 1 . Completeness Completeness was high across core variables. Numeric age (98.7%; 95% CI 98.3–99.0) and age group (98.7%; 95% CI 98.3–99.0) were nearly complete; sex was available in 98.6% (95% CI 98.2–98.9). Presenting complaint (97.2%; 95% CI 96.7–97.7), anatomical site (96.8%; 95% CI 96.2–97.3), and painkiller history (97.9%; 95% CI 97.4–98.3) were also well captured. Occupation had the highest missingness (5.1%; 95% CI 4.4–5.8). Missingness estimates with 95% CIs are provided in Table 1 . Validity Validity checks revealed very few errors. No numeric ages were outside 0–120 years (0.0%; 95% CI 0.0–0.1). No visit dates were recorded before 2000 or after the study period, although 21 dates were unparsable (0.5%; 95% CI 0.3–0.8). Non-standard sex values were observed in 57 cases (1.4%; 95% CI 1.1–1.8). Detailed validity results are shown in Table 1 . Table 1 Summarises missingness and validity violations; in text we report completeness (% present). Domain Variable / Rule Issue Type n % (95% CI) Completeness Numeric age Missing 54 1.3 (1.0–1.7) Age group (categorical) Missing 54 1.3 (1.0–1.7) Sex Missing/Invalid 57 1.4 (1.1–1.8) Occupation Missing 207 5.1 (4.4–5.8) Presenting complaint Missing 114 2.8 (2.3–3.3) Anatomical site Missing 130 3.2 (2.7–3.8) Painkiller history Missing 85 2.1 (1.7–2.6) Validity Visit date unparsable Violation 21 0.5 (0.3–0.8) Visit date not in future Violation 0 0.0 (0.0–0.1) Visit date before 2000 Violation 0 0.0 (0.0–0.1) Sex [Male, Female, Others/missing] Violation 57 1.4 (1.1–1.8) Age [0–120 years] Violation 0 0.0 (0.0–0.1) Consistency Identifier integrity was the principal weakness. We found seven complete-row duplicates and 131 duplicate serial numbers, indicating that identifiers were not enforced as unique. Age–age group concordance checks found 8 inconsistencies (0.2%; 95% CI 0.1–0.4), where the numeric age fell outside the expected categorical banding. Summary consistency findings are reported in Table 2 . Table 2 Internal consistency checks of orthopaedic OPD records (N = 4085) Consistency Dimension Finding n % (95% CI) Duplicate rows (full record) Yes 7.0 0.2 (0.1–0.3) Duplicate serial numbers Yes 131.0 3.2 (2.7–3.8) Age ↔ Age group concordance Inconsistent 8 0.2 (0.1–0.4) Discussion This audit demonstrates that routine orthopaedic outpatient data from a large public hospital in Bangladesh are broadly accurate and virtually EMR-ready. Central demographic fields—age, sex, and occupation—were entered with high completeness and validity, and implausible values were rare. These results are encouraging relative to others from LMICs, in which missing identifiers, invalid dates, or implausible ages were more frequent in emergency, radiology, and inpatient registers [ 15 ], [ 28 ]. These results suggest paper-based orthopaedic OPD registers have a good foundation from which electronic transition can take place. The largest weakness we identified was identifier integrity. Serial number duplicates and duplicate rows with low incidence pose the threat of double-counting and compromise longitudinal patient tracking. This is similar to issues of duplication documented in a population-based study of hypertension and diabetes in Brazil and in a patient's record for all inpatient and outpatient notes written within the University of Pennsylvania Health System, where human-numbered systems routinely induce duplication [ 29 ], [ 30 ]. A successful EMR protection, which includes automatic creation and database requirements for unique visit identifiers, eradicates this weakness outright [ 31 ], [ 32 ]. We also found modest gaps between the categorical age group and the numeric age because of human classification imperfections. Computerization of the derivation of age group from numeric age in an EMR would remove those errors, an approach already proposed as an EMR design methodology [ 33 ]. Presenting complaints were captured satisfactorily, but as free text, limiting their usefulness for analytics and clinical decision support. Coding these with a carefully selected subset of ICD-10, with free-text notes as an adjunct when appropriate, would reinforce clinical usability as well as analytics capability [ 34 ], [ 35 ]. These findings individually and collectively imply that a moderately sized set of judiciously selected EMR settings holds considerable promise to enhance data quality substantially. Compulsory data entry of basic demographics, constraints on date and age input, picklists on sex and complaints, and unique identifiers would reduce common errors without unduly burdening clinicians [ 36 ], [ 37 ]. Periodic monitoring of simple quality indicators—missingness in basic elements, duplicate identifiers, and percent of complaints coded—would provide an effective feedback loop enabling continuous improvement. There are also several strengths to the study. It validated a full six months of OPD data, applied audit rules that were reproducible, and reported confidence intervals even for rare errors. It has some limitations, which are a single-centre study, reliance on paper semi-structured registers, and a lack of controlled vocabularies for complaints. We also did not measure user experience or patient outcomes, something critical to future research in EMR adoption. As a whole, the KMCH orthopaedic OPD is already preserving the vast majority of what is needed for reliable digital records. With modest EMR protections, the department is able to transition from paper-based data capture to electronic with enhanced safety, the capability to capture accurate service metrics, and a foundation upon which scalable analytics across Bangladesh's tertiary care system can build out. Conclusion This audit found that routine orthopaedic OPD data at a large Bangladeshi tertiary hospital are largely usable, with targeted gaps that are amenable to straightforward EMR safeguards. The main vulnerabilities—duplicate identifiers and free-text presenting complaints—can be mitigated by unique visit IDs, constrained inputs, and ICD-10–aligned picklists with age-aware prompts. Implementing automatic age-group derivation from numeric age and making core fields mandatory should further stabilise data quality at the point of capture. Overall, the findings provide a practical blueprint for EMR configuration that can yield immediate gains in data reliability and support safer care, credible service metrics, and scalable analytics. Declarations Acknowledgement The authors would like to thank the administration and the Department of Orthopaedics of Khulna Medical College Hospital for granting permission to conduct this study and for access to patient records. Conflict of interest The authors declared no potential conflict of interest. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Informed consent Informed consent was waived due to the retrospective nature of the study and the use of anonymised patient data, as approved by the ethics committee. Ethical approval The study protocol was reviewed and approved by the Ethical Review Committee of Khulna Medical College Hospital, Khulna, Bangladesh (Reference No.: KMC/ERC/15; approval date: 6 th August 2025). All procedures were conducted in accordance with the ethical standards of the institutional and national research committee, as well as the 1964 Helsinki Declaration and its subsequent amendments. The committee waived the requirement for informed consent as anonymized routine outpatient records were used. Author contributions Md Ebrahim Molla: Conceptualisation, Methodology, Investigation, Data curation, Formal analysis, Visualisation, Writing - original draft, Md Nazrul Islam: Validation, Writing - review & editing, Rajib Kumar Paul: Writing - review & editing, Md Mohsin Ali Farazi: Supervision, Project administration, Resources, Writing - review & editing Availability of data and materials The datasets analysed during the current study are available from the corresponding author on reasonable request. References Syed MA, Azim SR, Baig M. Frequency of orthopedic problems among patients attending an orthopedic outpatient department: a retrospective analysis of 23 495 cases. Ann Saudi Med. May 2019;39(3):172–7. 10.5144/0256-4947.2019.172 . Briggs AM, et al. Reducing the global burden of musculoskeletal conditions. Bull World Health Organ. May 2018;96(5):366–8. 10.2471/BLT.17.204891 . Xie CX, et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7791604","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542157508,"identity":"8473fe1b-1ec6-4032-9524-ae8468ff4e04","order_by":0,"name":"Md Ebrahim 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Nazrul","lastName":"Islam","suffix":""},{"id":542157510,"identity":"9e0a8d5e-d0b2-4def-a45a-522b2bb53ece","order_by":2,"name":"Rajib Kumar Paul","email":"","orcid":"","institution":"Khulna Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rajib","middleName":"Kumar","lastName":"Paul","suffix":""},{"id":542157511,"identity":"042d614f-fc85-457e-868d-e3c62b2a76ab","order_by":3,"name":"Md Mohsin Ali Farazi","email":"","orcid":"","institution":"Khulna Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Mohsin Ali","lastName":"Farazi","suffix":""}],"badges":[],"createdAt":"2025-10-06 13:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7791604/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7791604/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95519322,"identity":"ee6bfce2-7f72-4be8-b8e6-913df64fb484","added_by":"auto","created_at":"2025-11-10 09:07:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":211072,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptMdEbrahimMollarevised.docx","url":"https://assets-eu.researchsquare.com/files/rs-7791604/v1/f806e58bae215a791a1906be.docx"},{"id":95529921,"identity":"d571678a-ad9b-4263-a78c-3578d27eb4a1","added_by":"auto","created_at":"2025-11-10 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09:07:38","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91681,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7791604/v1/5b2ecbbbfc7bc0d9f9a8a763.html"},{"id":95519319,"identity":"e5f8a129-9592-4e3f-a6ab-7a3f2aeb4bf1","added_by":"auto","created_at":"2025-11-10 09:07:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143041,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic characteristics of patients (N=4085)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7791604/v1/126fe855c8bb2cd540778e82.png"},{"id":95531700,"identity":"34e9b6cf-9f3f-4c13-addb-ac3d294b6acf","added_by":"auto","created_at":"2025-11-10 10:23:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":802672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7791604/v1/463bfc85-170e-4aea-b46e-a4a1d240a1f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Register to Electronic Medical Records: A Data-Quality Audit in a Bangladeshi Orthopaedic Outpatient Department","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMusculoskeletal conditions account for a large share of outpatient burden in tertiary hospitals across low- and middle-income countries, where orthopaedic clinics typically manage high caseloads under resource constraints [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In such settings, routine clinical records\u0026mdash;whether handwritten registers or semi-structured forms\u0026mdash;constitute the backbone for patient care, quality monitoring, and strategic planning [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The utility of these records depends critically on data quality: fields must be complete, valid, and internally consistent to support safe and reliable decision making [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eElectronic medical records (EMRs) are widely promoted to enhance data quality by enforcing structured data entry, real-time validation, and standardised terminologies[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. An emerging literature describes how EMR systems can reduce missingness, flag implausible values, and standardise coding, thus reducing transcription error and supporting high-quality clinical analytics [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For example, recent reviews emphasise that automation of data quality assessment (DQA) is gaining traction but remains fragmented and context-dependent [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In health registries and clinical data systems, frameworks for data quality (completeness, value conformance, consistency, plausibility) guide systematic auditing and remediation[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAcross clinical specialties in LMICs, audits consistently show variation in data completeness and conformance. A hospital-wide review in Pakistan found uneven completeness across departments, motivating tailored forms and validation rules [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In Ethiopia, emergency-department triage datasets exhibited distinct completeness profiles versus inpatient records, reinforcing the need for specialty-specific data-quality checks [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Recent methodological reviews of EMR data-quality assessment reinforce these findings, recommending domain-specific rules for completeness, value conformance, and plausibility rather than one-size-fits-all tools [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOrthopaedic records add unique challenges: clinical decisions often depend on integration with imaging systems (PACS), yet limited connectivity in LMICs can fragment records[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition, orthopaedic diagnosis and procedure coding are complex, with high potential for ambiguity if structured templates are absent [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Trauma and surgical registries in resource-constrained settings also highlight frequent identifier errors and incomplete operative details, strengthening the case for structured data capture at the point of care [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite national digital health initiatives in Bangladesh, many health departments continue to rely on paper-based or semi-electronic registers with heterogeneous field definitions and free-text entries due to a lack of a robust digital infrastructure [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. WHO\u0026rsquo;s review of digital health in Bangladesh notes that digital registry adoption remains uneven despite policy support [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Few empirical audits in hospital settings in LMICs exist that quantify register deficiencies and map those deficiencies to practical EMR safeguards [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This gap both limits confidence in the secondary use of legacy data and complicates the design of EMR forms that are resilient to real-world workflow constraints. Similar challenges in register standardisation have been documented in Bangladesh\u0026rsquo;s newborn and inpatient registries, motivating national efforts toward standard registers [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo fill this gap, we conducted a structured data-quality audit of the Orthopaedic Outpatient Department (OPD) at Khulna Medical College Hospital (KMCH), a large public tertiary centre. Drawing from the complete OPD register export, we assessed three key dimensions of data quality\u0026mdash;completeness, validity (format, value plausibility, and categorical constraints), and internal consistency (duplicate detection, age\u0026ndash;age-group concordance). We then translated observed deficiencies into a concise set of EMR configuration rules: mandatory core fields, bounded date pickers, numeric age with auto-derived age group, ICD-10 picklists, and database-enforced unique visit identifiers.\u003c/p\u003e\u003cp\u003eThe audit was designed for maximal translatability: we report field-level missingness and violation rates with 95% confidence intervals, but do not modify raw data, thus preserving audit objectivity. Our Results show excellent capture of core demographics, a few unparsable dates, zero out-of-range age values, but notable duplication among visit identifiers. These findings not only establish a baseline but also offer a direct blueprint for EMR form and rule design in busy orthopaedic OPDs.\u003c/p\u003e\u003cp\u003eOur study aimed to quantify completeness, validity, and internal consistency of core fields in the KMCH orthopaedic OPD register and translate audit findings into a prioritised set of EMR validation and configuration rules suitable for routine deployment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective data quality audit of routine outpatient visit records from the Orthopaedic Outpatient Department (OPD) of Khulna Medical College Hospital (KMCH), Khulna, Bangladesh. KMCH is a 500-bed tertiary teaching hospital serving Khulna city and surrounding districts, which has to provide healthcare services far beyond its designated capacity. The audit encompassed all available records from the OPD register export, covering the period from 1 January 2025 to 31 June 2025. The primary objective was to quantify the completeness, validity, and internal consistency of core data elements and translate identified quality gaps into implementable electronic medical record (EMR) validation rules suitable for a tertiary care orthopaedic setting.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Source and Variables\u003c/h3\u003e\n\u003cp\u003eThe OPD register was maintained in a structured paper logbook during routine care and transcribed monthly by trained hospital staff into a hospital-managed Excel file (“Data.xlsx”). No EMR system was in routine use at KMCH during the audit period. For this audit, we used the full exported dataset. Prior to analysis, we performed random 5% spot checks against the original paper register to verify transcription accuracy. In addition, internal logic rules (date plausibility, numeric age range, and age–age group concordance) were applied as quality-control checks. The analysis dataset was de-identified before audit, and all transformations were performed on analysis copies to preserve the source file.\u003c/p\u003e\n\u003ch3\u003eData Pre-processing\u003c/h3\u003e\n\u003cp\u003eSeveral pre-processing steps were performed to ensure data integrity: (i) value normalization, with sex recoded to standardized categories (Male/Female/Other) and illegible entries treated as invalid; (ii) permissive date parsing using multiple common formats; (iii) numeric age coercion with range validation; and (iv) duplicate identification without removal. Duplicate records were flagged at two levels: complete row duplicates (identical across all fields) and serial number duplicates (regardless of other field values) to detect identifier collisions. Age group codes were retained as categorical labels since code-to-band mapping documentation was unavailable.\u003c/p\u003e\n\u003ch3\u003eData Quality Assessment Framework\u003c/h3\u003e\n\u003cp\u003eWe evaluated three established data quality dimensions following international healthcare data quality standards.\u003c/p\u003e\n\u003ch3\u003eCompleteness Assessment\u003c/h3\u003e\n\u003cp\u003eCompleteness was quantified as the proportion of records containing valid, non-missing content for each variable. Missing data included empty fields, null values, unparsable entries, and non-standard categorical values.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eValidity Assessment\u003c/h2\u003e\u003cp\u003eFormat and value validity were assessed using pre-specified clinical and logical rules:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTemporal validity\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eVisit dates before 1 January 2020 or after the data collection endpoint were flagged as implausible\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDemographic validity\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eNumeric age outside the 0-120 year range was considered invalid\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCategorical validity\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eSex values outside the standard {Male, Female, Other} categories were flagged\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData format validity\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eUnparsable date formats were enumerated\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eConsistency Assessment\u003c/h3\u003e\n\u003cp\u003eInternal consistency evaluation focused on identifier logic, specifically duplicate serial number detection. We also performed age–age group concordance checks, comparing numeric age values against categorical group assignments to detect mismatches. Presenting complaints were analyzed using exploratory keyword screening to demonstrate potential EMR age-appropriate clinical decision support implementation.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were summarized using mean, standard deviation, median, interquartile range, and 5th and 95th percentiles. Data quality metrics were reported as proportions with 95% confidence intervals calculated using the Wilson score method, which provides superior coverage for extreme probabilities compared to normal approximation methods. No hypothesis testing was performed as the study objective was descriptive estimation rather than statistical inference.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eImplementation-Focused Analysis\u003c/h2\u003e\u003cp\u003eEach identified data quality issue was systematically mapped to specific EMR configuration recommendations: mandatory field validation with save-prevention for core data elements, constrained input controls (bounded date pickers, numeric ranges), standardised picklists for sex and presenting complaints aligned with ICD-10 classifications, automatic derivations (age group from numeric age), and database-level uniqueness constraints for visit identifiers.\u003c/p\u003e\u003cp\u003eThe robustness of date parsing was evaluated using multiple format recognition strategies. Duplicate detection employed both complete-record and identifier-based definitions to ensure comprehensive coverage. All analyses were performed using Python (pandas, NumPy, SciPy libraries), with Wilson confidence intervals computed via scipy.stats.binomtest (...).proportion_ci (method='wilson')\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEthical Considerations\u003c/h2\u003e\u003cp\u003eThis retrospective audit of de-identified administrative data was conducted in accordance with institutional guidelines for quality improvement activities. The study focused on data quality assessment rather than patient-level analysis, with all findings reported in aggregate form to protect patient confidentiality. Ethical clearance for the study was granted by the Institutional Ethical Review Board of Khulna Medical College, Bangladesh (Reference No.: KMC/ERC/15; approval date: 6th August 2025).\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003ch2\u003eDemographic snapshot\u003c/h2\u003e\u003cp\u003eSex was recorded as female in 2,066 (50.6%) and male in 1,962 (48.0%), with 57 entries missing/invalid (1.4%; 95% CI 1.1–1.8). Numeric age was available for 4,031/4,085 visits (98.7%; 95% CI 98.3–99.0); 54/4,085 were missing (1.3%; 95% CI 1.0–1.7). The age distribution had median of 40 years (IQR 27–52), a mean of 39.4 (SD 17.6), and a range of 0–100. Occupation was recorded for most patients, with homemakers 1623 (39.7%), students 742 (18.2%), and labourers 731 (17.9%) predominating; 207 (5.1%) of occupation fields were missing (95% CI 4.4–5.8). The overall demographic distributions of sex, age, and occupation are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eCompleteness\u003c/h2\u003e\u003cp\u003eCompleteness was high across core variables. Numeric age (98.7%; 95% CI 98.3–99.0) and age group (98.7%; 95% CI 98.3–99.0) were nearly complete; sex was available in 98.6% (95% CI 98.2–98.9). Presenting complaint (97.2%; 95% CI 96.7–97.7), anatomical site (96.8%; 95% CI 96.2–97.3), and painkiller history (97.9%; 95% CI 97.4–98.3) were also well captured. Occupation had the highest missingness (5.1%; 95% CI 4.4–5.8). Missingness estimates with 95% CIs are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eValidity\u003c/h2\u003e\u003cp\u003eValidity checks revealed very few errors. No numeric ages were outside 0–120 years (0.0%; 95% CI 0.0–0.1). No visit dates were recorded before 2000 or after the study period, although 21 dates were unparsable (0.5%; 95% CI 0.3–0.8). Non-standard sex values were observed in 57 cases (1.4%; 95% CI 1.1–1.8). Detailed validity results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\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\u003eSummarises missingness and validity violations; in text we report completeness (% present).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDomain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable / Rule\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIssue Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eCompleteness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumeric age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.3 (1.0–1.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge group (categorical)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.3 (1.0–1.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMissing/Invalid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.4 (1.1–1.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.1 (4.4–5.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePresenting complaint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.8 (2.3–3.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnatomical site\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.2 (2.7–3.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePainkiller history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.1 (1.7–2.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eValidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVisit date unparsable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eViolation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5 (0.3–0.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVisit date not in future\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eViolation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0 (0.0–0.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVisit date before 2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eViolation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0 (0.0–0.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSex [Male, Female, Others/missing]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eViolation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.4 (1.1–1.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge [0–120 years]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eViolation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0 (0.0–0.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eConsistency\u003c/h2\u003e\u003cp\u003eIdentifier integrity was the principal weakness. We found seven complete-row duplicates and 131 duplicate serial numbers, indicating that identifiers were not enforced as unique. Age–age group concordance checks found 8 inconsistencies (0.2%; 95% CI 0.1–0.4), where the numeric age fell outside the expected categorical banding. Summary consistency findings are reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\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\u003eInternal consistency checks of orthopaedic OPD records (N = 4085)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsistency Dimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinding\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuplicate rows (full record)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2 (0.1–0.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuplicate serial numbers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.2 (2.7–3.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge ↔ Age group concordance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInconsistent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2 (0.1–0.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis audit demonstrates that routine orthopaedic outpatient data from a large public hospital in Bangladesh are broadly accurate and virtually EMR-ready. Central demographic fields\u0026mdash;age, sex, and occupation\u0026mdash;were entered with high completeness and validity, and implausible values were rare. These results are encouraging relative to others from LMICs, in which missing identifiers, invalid dates, or implausible ages were more frequent in emergency, radiology, and inpatient registers [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These results suggest paper-based orthopaedic OPD registers have a good foundation from which electronic transition can take place.\u003c/p\u003e\u003cp\u003eThe largest weakness we identified was identifier integrity. Serial number duplicates and duplicate rows with low incidence pose the threat of double-counting and compromise longitudinal patient tracking. This is similar to issues of duplication documented in a population-based study of hypertension and diabetes in Brazil and in a patient's record for all inpatient and outpatient notes written within the University of Pennsylvania Health System, where human-numbered systems routinely induce duplication [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A successful EMR protection, which includes automatic creation and database requirements for unique visit identifiers, eradicates this weakness outright [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe also found modest gaps between the categorical age group and the numeric age because of human classification imperfections. Computerization of the derivation of age group from numeric age in an EMR would remove those errors, an approach already proposed as an EMR design methodology [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Presenting complaints were captured satisfactorily, but as free text, limiting their usefulness for analytics and clinical decision support. Coding these with a carefully selected subset of ICD-10, with free-text notes as an adjunct when appropriate, would reinforce clinical usability as well as analytics capability [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese findings individually and collectively imply that a moderately sized set of judiciously selected EMR settings holds considerable promise to enhance data quality substantially. Compulsory data entry of basic demographics, constraints on date and age input, picklists on sex and complaints, and unique identifiers would reduce common errors without unduly burdening clinicians [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Periodic monitoring of simple quality indicators\u0026mdash;missingness in basic elements, duplicate identifiers, and percent of complaints coded\u0026mdash;would provide an effective feedback loop enabling continuous improvement.\u003c/p\u003e\u003cp\u003eThere are also several strengths to the study. It validated a full six months of OPD data, applied audit rules that were reproducible, and reported confidence intervals even for rare errors. It has some limitations, which are a single-centre study, reliance on paper semi-structured registers, and a lack of controlled vocabularies for complaints. We also did not measure user experience or patient outcomes, something critical to future research in EMR adoption.\u003c/p\u003e\u003cp\u003eAs a whole, the KMCH orthopaedic OPD is already preserving the vast majority of what is needed for reliable digital records. With modest EMR protections, the department is able to transition from paper-based data capture to electronic with enhanced safety, the capability to capture accurate service metrics, and a foundation upon which scalable analytics across Bangladesh's tertiary care system can build out.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis audit found that routine orthopaedic OPD data at a large Bangladeshi tertiary hospital are largely usable, with targeted gaps that are amenable to straightforward EMR safeguards. The main vulnerabilities\u0026mdash;duplicate identifiers and free-text presenting complaints\u0026mdash;can be mitigated by unique visit IDs, constrained inputs, and ICD-10\u0026ndash;aligned picklists with age-aware prompts. Implementing automatic age-group derivation from numeric age and making core fields mandatory should further stabilise data quality at the point of capture. Overall, the findings provide a practical blueprint for EMR configuration that can yield immediate gains in data reliability and support safer care, credible service metrics, and scalable analytics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the administration and the Department of Orthopaedics of Khulna Medical College Hospital for granting permission to conduct this study and for access to patient records.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was waived due to the retrospective nature of the study and the use of anonymised patient data, as approved by the ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Ethical Review Committee of Khulna Medical College Hospital, Khulna, Bangladesh (Reference No.: KMC/ERC/15; approval date: 6\u003csup\u003eth\u003c/sup\u003e August 2025). All procedures were conducted in accordance with the ethical standards of the institutional and national research committee, as well as the 1964 Helsinki Declaration and its subsequent amendments. The committee waived the requirement for informed consent as anonymized routine outpatient records were used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMd Ebrahim Molla:\u003c/strong\u003e Conceptualisation, Methodology, Investigation, Data curation, Formal analysis,\u0026nbsp;Visualisation,\u0026nbsp;Writing - original draft,\u0026nbsp;\u003cstrong\u003eMd Nazrul Islam:\u003c/strong\u003e Validation, Writing - review \u0026amp; editing, \u003cstrong\u003eRajib Kumar Paul:\u003c/strong\u003e Writing - review \u0026amp; editing,\u0026nbsp;\u003cstrong\u003eMd Mohsin Ali Farazi:\u003c/strong\u003e Supervision, Project administration, Resources, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSyed MA, Azim SR, Baig M. 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The use of an electronic health record system reduces errors in the National Hip Fracture Database, \u003cem\u003eAge Ageing\u003c/em\u003e, vol. 48, no. 2, pp. 285\u0026ndash;290, Mar. 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ageing/afy177\u003c/span\u003e\u003cspan address=\"10.1093/ageing/afy177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Data quality audit, Electronic medical records, Orthopaedics, Health informatics, Low- and middle-income countries, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-7791604/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7791604/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eElectronic medical records (EMRs) are promoted to improve data quality, yet most hospitals in low- and middle-income countries (LMICs) still rely on paper or semi-electronic registers. Orthopaedic outpatient departments face unique documentation challenges due to complex coding, high caseloads, and limited digital integration. Evidence from empirical audits that quantify register deficiencies and map them to EMR safeguards remains sparse in Bangladesh and comparable LMIC settings.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective audit of all orthopaedic outpatient records (N\u0026thinsp;=\u0026thinsp;4,085) at Khulna Medical College Hospital, Bangladesh, from 1 January to 30 June 2025. Three dimensions of data quality were assessed: completeness, validity (format, plausibility, categorical conformance), and internal consistency (duplicate detection, age\u0026ndash;age group concordance). Missingness and violation rates were quantified with 95% Wilson confidence intervals. Identified deficiencies were translated into EMR configuration recommendations, including mandatory fields, constrained inputs, ICD-10 picklists, and database-enforced unique identifiers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCore demographic fields demonstrated high completeness: sex 98.6%, numeric age 98.7%, and occupation 94.9%. Presenting complaint, anatomical site, and painkiller history exceeded 96% completeness. Validity was strong, with no implausible ages (0\u0026ndash;120 years) and only 0.5% unparsable dates. Identifier integrity was the principal weakness: 131 duplicate serial numbers (3.2%) and seven complete-row duplicates were detected. Age\u0026ndash;age group mismatches were rare (0.2%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOrthopaedic outpatient registers in this high-volume tertiary hospital are broadly EMR-ready, with most fields accurate and complete. Key vulnerabilities\u0026mdash;duplicate identifiers and free-text complaints\u0026mdash;can be addressed by unique visit IDs, constrained input controls, and ICD-10\u0026ndash;aligned coding. Findings provide a practical blueprint for EMR configuration in resource-constrained settings, offering immediate pathways to safer care, credible service metrics, and scalable digital health analytics.\u003c/p\u003e","manuscriptTitle":"From Register to Electronic Medical Records: A Data-Quality Audit in a Bangladeshi Orthopaedic Outpatient Department","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 09:07:33","doi":"10.21203/rs.3.rs-7791604/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"120647566354697033696123064373356319648","date":"2025-11-05T01:20:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T11:15:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-09T13:54:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T05:59:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-08T05:58:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-10-06T12:58:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d472f853-bedc-4632-8975-f39178ac5ea4","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T09:07:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 09:07:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7791604","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7791604","identity":"rs-7791604","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}