Health information management : journal of the Health Information Management Association of Australia最新文献

{"title":"Family violence homicide rates: a state-wide comparison of three data sources in Victoria, Australia.","authors":"Reena Sarkar,&nbsp;Joanna F Dipnall,&nbsp;Richard Bassed,&nbsp;Joan Ozanne-Smith Ao","doi":"10.1177/18333583211060464","DOIUrl":"https://doi.org/10.1177/18333583211060464","url":null,"abstract":"<p><strong>Background: </strong>Family violence homicide (FVH) is a major public health and social problem in Australia. FVH trend rates are key outcomes that determine the effectiveness of current management practices and policy directions. Data source-related methodological problems affect FVH research and policy and the reliable measurement of homicide trends.</p><p><strong>Objective: </strong>This study aimed to determine data reliability and temporal trends of Victorian FVH rates and sex and relationship patterns.</p><p><strong>Method: </strong>FVH rates per 100,000 persons in Victoria were compared between the National Coronial Information System (NCIS), Coroners Court of Victoria (CCoV) Homicide Register, and the National Homicide Monitoring Program (NHMP). Trends for 2001-2017 were analysed using Joinpoint regression. Crude rates were determined by sex and relationship categories using annual frequencies and Australian Bureau of Statistics population estimates.</p><p><strong>Results: </strong>NCIS closed FVH cases totalled 360, and an apparent downward trend in the FVH rate was identified. However, CCoV and NHMP rates trended upwards. While NCIS and CCoV were case-based, NHMP was incident-based, contributing to rate variations. The NCIS-derived trend was particularly impacted by unavailable case data, potential coding errors and entry backlog. Neither CCoV nor NHMP provided victim-age in their public domain data to enable age-adjusted rate comparison.</p><p><strong>Conclusion: </strong>Current datasets have limitations for FVH trend determination; most notably lag times for NCIS data.</p><p><strong>Implications: </strong>This study identified an indicative upward trend in FVH rates in Victoria, suggesting insufficiency of current management and policy settings for its prevention and control.</p>","PeriodicalId":73210,"journal":{"name":"Health information management : journal of the Health Information Management Association of Australia","volume":"52 3","pages":"135-143"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10173854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The suitability of government health information assets for secondary use in research: A fit-for-purpose analysis.","authors":"Merilyn Riley,&nbsp;Kerin Robinson,&nbsp;Monique F Kilkenny,&nbsp;Sandra G Leggat","doi":"10.1177/18333583221078377","DOIUrl":"https://doi.org/10.1177/18333583221078377","url":null,"abstract":"<p><strong>Background: </strong>Governments have responsibility for ensuring the quality and fitness-for-purpose of personal health data provided to them. While these health information assets are used widely for research, this secondary usage has received minimal research attention.</p><p><strong>Objective: </strong>This study aimed to investigate the secondary uses, in research, of population health and administrative datasets (information assets) of the Department of Health (DoH), Victoria, Australia. The objectives were to (i) identify research based on these datasets published between 2008 and 2020; (ii) describe the data quality studies published between 2008 and 2020 for each dataset and (iii) evaluate \"fitness-for-purpose\" of the published research.</p><p><strong>Method: </strong>Using a modified scoping review, research publications from 2008 to 2020 based on information assets related to health service provision and containing person-level data were reviewed. Publications were summarised by data quality and purpose-categories based on a taxonomy of data use. Fitness-for-purpose was evaluated by comparing the publicly stated purpose(s) for which each information asset was collected, with the purpose(s) assigned to the published research.</p><p><strong>Results: </strong>Of the >1000 information assets, 28 were utilised in 756 publications: 54% were utilised for general research purposes, 14% for patient safety, 10% for quality of care and 39% included data quality-related publications. Almost 85% of publications used information assets that were fit-for-purpose.</p><p><strong>Conclusion: </strong>The DoH information assets were used widely for secondary purposes, with the majority identified as fit-for-purpose. We recommend that data custodians, including governments, provide information on data quality and transparency on data use of their health information assets.</p>","PeriodicalId":73210,"journal":{"name":"Health information management : journal of the Health Information Management Association of Australia","volume":"52 3","pages":"157-166"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10183132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of root causes of clinical coding problems in Iranian hospitals.","authors":"Mohamad Jebraeily,&nbsp;Jebraeil Farzi,&nbsp;Shahla Fozoonkhah,&nbsp;Abbas Sheikhtaheri","doi":"10.1177/18333583211060480","DOIUrl":"https://doi.org/10.1177/18333583211060480","url":null,"abstract":"<p><strong>Background: </strong>Improving the quality of coded data requires the identification and evaluation of the root causes of clinical coding problems to inform appropriate solutions.</p><p><strong>Objective: </strong>The objective of this study was to identify the root causes of clinical coding problems.</p><p><strong>Method: </strong>Twenty-one clinical coders from three cities in Iran were interviewed. The five formal categories in Ishikawa's cause-and-effect diagram were applied as pre-determined themes for the data analysis.</p><p><strong>Results: </strong>The study indicated 16 root causes of clinical coding problems in the five main themes: (i) policies, protocols, and processes (lack of clinical documentation guidelines; lack of audit of clinical coding and feedback to clinical coders; the long interval between documentation and clinical coding; and not using coded data for reimbursement; (ii) individual factors (shortage of clinical coders; low-skilled clinical coders; clinical coders' insufficient communication with physicians; and the lack of continuing education; (iii) equipment and materials (incomplete medical records; lack of access to electronic medical records and electronic coding support tools; (iv) working environment (lack of an appropriate, dynamic, and motivational workspace; and (v) management factors (mangers' inattention to the importance of coding and clinical documentation; and to providing the required staff support.</p><p><strong>Conclusion: </strong>The study identified 16 root causes of clinical coding problems that stand in the way of clinical coding quality improvement.</p><p><strong>Implications: </strong>The quality of clinical coding could be improved by hospital managers and health policymakers taking these problems into account to develop strategies and implement solutions that target the root causes of clinical coding problems.</p>","PeriodicalId":73210,"journal":{"name":"Health information management : journal of the Health Information Management Association of Australia","volume":"52 3","pages":"144-150"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10527713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and implementation of an institutional enhanced recovery program data process.","authors":"Mohamed A Seif,&nbsp;Brittany C Kruse,&nbsp;Cameron A Keramati,&nbsp;Thomas A Aloia,&nbsp;Ruth A Amaku,&nbsp;Shreyas Bhavsar,&nbsp;Kenneth R DeCarlo,&nbsp;Rose Joan D Erfe,&nbsp;Jarrod S Eska,&nbsp;Maria D Iniesta,&nbsp;Laura R Prakash,&nbsp;Tao Zhang,&nbsp;Vijaya Gottumukkala","doi":"10.1177/18333583221095139","DOIUrl":"https://doi.org/10.1177/18333583221095139","url":null,"abstract":"<p><p><b>Background:</b> With increasing implementation of enhanced recovery programs (ERPs) in clinical practice, standardised data collection and reporting have become critical in addressing the heterogeneity of metrics used for reporting outcomes. Opportunities exist to leverage electronic health record (EHR) systems to collect, analyse, and disseminate ERP data. <b>Objectives:</b> (i) To consolidate relevant ERP variables into a singular data universe; (ii) To create an accessible and intuitive query tool for rapid data retrieval. <b>Method:</b> We reviewed nine established individual team databases to identify common variables to create one standard ERP data dictionary. To address data automation, we used a third-party business intelligence tool to map identified variables within the EHR system, consolidating variables into a single ERP universe. To determine efficacy, we compared times for four experienced research coordinators to use manual, five-universe, and ERP Universe processes to retrieve ERP data for 10 randomly selected surgery patients. <b>Results:</b> The total times to process data variables for all 10 patients for the manual, five universe, and ERP Universe processes were 510, 111, and 76 min, respectively. Shifting from the five-universe or manual process to the ERP Universe resulted in decreases in time of 32% and 85%, respectively. <b>Conclusion:</b> The ERP Universe improves time spent collecting, analysing, and reporting ERP elements without increasing operational costs or interrupting workflow. <b>Implications:</b> Manual data abstraction places significant burden on resources. The creation of a singular instrument dedicated to ERP data abstraction greatly increases the efficiency in which clinicians and supporting staff can query adherence to an ERP protocol.</p>","PeriodicalId":73210,"journal":{"name":"Health information management : journal of the Health Information Management Association of Australia","volume":"52 3","pages":"151-156"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10545700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating data quality in the Australian and New Zealand dialysis and transplant registry using administrative hospital admission datasets and data-linkage.","authors":"Dharmenaan Palamuthusingam,&nbsp;Elaine M Pascoe,&nbsp;Carmel M Hawley,&nbsp;David W Johnson,&nbsp;Gishan Ratnayake,&nbsp;Stephen McDonald,&nbsp;Neil Boudville,&nbsp;Matthew Jose,&nbsp;Magid Fahim","doi":"10.1177/18333583221097724","DOIUrl":"https://doi.org/10.1177/18333583221097724","url":null,"abstract":"<p><p><b>Background:</b> Clinical quality registries provide rich and useful data for clinical quality monitoring and research purposes but are susceptible to data quality issues that can impact their usage. <b>Objective</b>: This study assessed the concordance between comorbidities recorded in the Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry and those in state-based hospital admission datasets. <b>Method:</b> All patients in New South Wales, South Australia, Tasmania, Victoria and Western Australia recorded in ANZDATA as requiring chronic kidney replacement therapy (KRT) between 01/07/2000 and 31/12/2015 were linked with state-based hospital admission datasets. Coronary artery disease, diabetes mellitus, cerebrovascular disease, chronic lung disease and peripheral vascular disease recorded in ANZDATA at each annual census date were compared overall, over time and between different KRT modalities to comorbidities recorded in hospital admission datasets, as defined by the International Classification of Diseases (ICD-10-AM), using both the kappa statistic and logistic regression analysis. <b>Results:</b> 29, 334 patients with 207,369 hospital admissions were identified. Comparison was made at census date for every patient comparison. Overall agreement was \"very good\" for diabetes mellitus (92%, k = 0.84) and \"poor\" to \"fair\" (21-61%, k = 0.02-0.22) for others. Diabetes mellitus recording had the highest accuracy (sensitivity 93% (±SE 0.2) and specificity 93% (±SE 0.2)), and cerebrovascular disease had the lowest (sensitivity 54% (±SE 0.2) and specificity 21% (±SE 0.3)). The false positive rates for cerebrovascular disease, peripheral vascular disease and chronic airway disease ranged between 18 and 33%. The probability of a false positive was lowest for kidney transplant patients for all comorbidities and highest for patients on haemodialysis. <b>Conclusions and Implications:</b> Agreement between the clinical quality registry and hospital admission datasets was variable, with the prevalence of comorbidities being higher in ANZDATA.</p>","PeriodicalId":73210,"journal":{"name":"Health information management : journal of the Health Information Management Association of Australia","volume":"52 3","pages":"212-220"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10545704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensiveness, accuracy, quality, credibility and readability of online information about knee osteoarthritis.","authors":"Anthony J Goff,&nbsp;Christian J Barton,&nbsp;Mark Merolli,&nbsp;Andre Shi Zhang Quah,&nbsp;Caleb Ki-Cheong Hoe,&nbsp;Danilo De Oliveira Silva","doi":"10.1177/18333583221090579","DOIUrl":"https://doi.org/10.1177/18333583221090579","url":null,"abstract":"<p><strong>Background: </strong>People are increasingly using the Internet to retrieve health information about chronic musculoskeletal conditions, yet content can be inaccurate and of variable quality.</p><p><strong>Objective: </strong>To summarise (i) comprehensiveness, (ii) accuracy and clarity, iii) quality of information about treatment choices, (iv) credibility and (v) readability of online information about knee osteoarthritis.</p><p><strong>Method: </strong>Systematic appraisal of website content. Searches for \"knee osteoarthritis\" and \"knee arthritis\" were performed using Google and Bing (October 2020). The top 20 URLs of each search were screened for eligibility. Comprehensiveness, accuracy and clarity of content were matched against 14 pre-defined topic descriptors. DISCERN and HONcode were used to measure quality of information about treatment choices and website credibility, respectively. Flesch Reading Ease and Flesch-Kincaid Grade Level tests were used to assess readability.</p><p><strong>Results: </strong>Thirty-five websites were included. Websites were generally comprehensive (median, range = 12, 0-14/14) with descriptors available for 67% (<i>n</i> = 330/490) of topics across all websites, but only 35% (<i>n</i> = 116/330) were accurate and clear. Quality of information about treatment choices was generally low (median DISCERN score, range = 40, 16-56/80). Credibility descriptors were present for 65% (<i>n</i> = 181/280) of items, with 81% (<i>n</i> = 146/181) of descriptors being clear. Median Flesch reading ease was 53 (range = 21-74), and Flesch-Kincaid grade level was 8 (range = 5-11).</p><p><strong>Conclusion: </strong>Few websites provide accurate and clear content aligned to key research evidence. Quality of information about treatment choices was poor, with large variation in comprehensiveness, credibility and readability.</p><p><strong>Implications: </strong>Careful consideration is required by clinicians to identify what online information people with knee osteoarthritis have accessed and to address misinformed beliefs.</p>","PeriodicalId":73210,"journal":{"name":"Health information management : journal of the Health Information Management Association of Australia","volume":"52 3","pages":"185-193"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10172350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of routine blood alcohol tests and ICD-10-AM coding of alcohol involvement for major trauma patients.","authors":"Georgina Lau,&nbsp;Belinda J Gabbe,&nbsp;Biswadev Mitra,&nbsp;Paul M Dietze,&nbsp;Sandra Braaf,&nbsp;Ben Beck","doi":"10.1177/18333583211037171","DOIUrl":"https://doi.org/10.1177/18333583211037171","url":null,"abstract":"<p><strong>Background: </strong>Alcohol use is a key preventable risk factor for serious injury. To effectively prevent alcohol-related injuries, we rely on the accurate surveillance of alcohol involvement in injury events. This often involves the use of administrative data, such as International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) coding.</p><p><strong>Objective: </strong>To evaluate the completeness and accuracy of using administrative coding for the surveillance of alcohol involvement in major trauma injury events by comparing patient blood alcohol concentration (BAC) with ICD-10-AM coding.</p><p><strong>Method: </strong>This retrospective cohort study examined 2918 injury patients aged ≥18 years who presented to a major trauma centre in Victoria, Australia, over a 2-year period, of which 78% (<i>n</i> = 2286) had BAC data available.</p><p><strong>Results: </strong>While 15% of patients had a non-zero BAC, only 4% had an ICD-10-AM code suggesting acute alcohol involvement. The agreement between blood alcohol test results and ICD-10-AM coding of acute alcohol involvement was fair (<i>κ</i> = 0.33, 95% confidence interval: 0.27-0.38). Of the 341 patients with a non-zero BAC, 82 (24.0%) had ICD-10-AM codes related to acute alcohol involvement. Supplementary factors Y90 Evidence of alcohol involvement determined by blood alcohol level codes, which specifically describe patient BAC, were assigned to just 29% of eligible patients with a non-zero BAC.</p><p><strong>Conclusion: </strong>ICD-10-AM coding underestimated the proportion of alcohol-related injuries compared to patient BAC.</p><p><strong>Implications: </strong>Given the current role of administrative data in the surveillance of alcohol-related injuries, these findings may have significant implications for the implementation of cost-effective strategies for preventing alcohol-related injuries.</p>","PeriodicalId":73210,"journal":{"name":"Health information management : journal of the Health Information Management Association of Australia","volume":"52 2","pages":"112-118"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9418684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discharge status of the patient: evaluating hospital data quality with a focus on long-term and palliative care patient data.","authors":"João Vasco Santos,&nbsp;Filipa Santos Martins,&nbsp;Fernando Lopes,&nbsp;Júlio Souza,&nbsp;Alberto Freitas","doi":"10.1177/18333583211054161","DOIUrl":"https://doi.org/10.1177/18333583211054161","url":null,"abstract":"Dear Editor, Health administrative data, as found in hospital morbidity datasets are valuable data sources that inform epidemiological studies such as the Global Burden of Disease study (GBD 2019 Diseases and Injuries Collaborators, 2020), and can be used to achieve many aims in relation to health services research and management. Furthermore, Diagnosis Related Group (DRG) systems rely on administrative data, namely diagnosis/procedure codes, age, sex, and discharge destination (Averill et al., 2003) and in many countries are used for hospital reimbursement purposes (Geissler et al., 2011; Mathauer and Wittenbecher, 2013). In this context, the quality of health records, which constitutes the basis for the construction of administrative datasets through clinical coding (Alonso et al., 2020), is paramount. Clinical coding quality issues have been widely discussed (Cheng et al., 2009; Dafny, 2005; O’Malley et al., 2005; Pongpirul and Robinson, 2013; Southern et al., 2015), but little attention has been paid to issues associated with some administrative variables, such as discharge destination, despite their potential impact on the financial reimbursements received by hospitals, as previously mentioned in the case of Medicare (Centers for Medicare & Medicaid Services, 2018). Presented in this letter is our analysis of the quality of this variable, which is essential for DRG grouping and can also be reused for many other purposes. Discharge destination, as a variable, is currently categorised according to standard codes, using information abstracted from hospital documentation. We assessed 2016 data from the Portuguese Hospital Morbidity Database (HMD), which includes administrative data collected from all mainland public and public–private partnership hospitals (62 institutions). These data, described as hospital discharges, were compared to referrals to long-term and palliative care as recorded in the National Network for Long-Term Care (Rede Nacional de Cuidados Continuados Integrados – RNCCI) dataset. RNCCI data are obtained from GestCare, an information system that is used to record all RNCCI-related procedures, including referral. RNCCI data, as accessed through the Portuguese National Health Service Transparency Portal (Ministry of Health, 2016), will be therefore mentioned as referrals. As more than 90% of the referrals originate from hospitals (ACSS, 2017), data from the HMD should correspond with this data source. We focused on the quality of data related to hospital referral for long-term and palliative care, which in Portugal is overseen by the RNCCI (D.R., 2006; Lopes et al., 2018). From the variable ‘discharge destination’, discharges to long-term and palliative care categories were chosen due to data availability, as these were the only data categories that have a secondary information source, with which comparisons can be made. We selected HMD categories ‘63 –Discharge to long-term inpatient care’ and ‘51 – Discharge to Palliative Care","PeriodicalId":73210,"journal":{"name":"Health information management : journal of the Health Information Management Association of Australia","volume":"52 2","pages":"125-127"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9418694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Training and experience of coding with the World Health Organization's International Classification of Diseases, Eleventh Revision.","authors":"Cathy A Eastwood,&nbsp;Danielle A Southern,&nbsp;Chelsea Doktorchik,&nbsp;Shahreen Khair,&nbsp;Denise Cullen,&nbsp;Alicia Boxill,&nbsp;Malgorzata Maciszewski,&nbsp;Lucia Otero Varela,&nbsp;William Ghali,&nbsp;Lori Moskal,&nbsp;Hude Quan","doi":"10.1177/18333583211038633","DOIUrl":"https://doi.org/10.1177/18333583211038633","url":null,"abstract":"<p><strong>Background: </strong>The new International Classification of Diseases, Eleventh Revision for Mortality and Morbidity Statistics (ICD-11) was developed and released by the World Health Organization (WHO) in June 2018. Because ICD-11 incorporates new codes and features, training materials for coding with ICD-11 are urgently needed prior to its implementation.</p><p><strong>Objective: </strong>This study outlines the development of ICD-11 training materials, training processes and experiences of clinical coders while learning to code using ICD-11.</p><p><strong>Method: </strong>Six certified clinical coders were recruited to code inpatient charts using ICD-11. Training materials were developed with input from experts from the Canadian Institute for Health Information and the WHO, and the clinical coders were trained to use the new classification. Monthly team meetings were conducted to enable discussions on coding issues and to select the correct ICD-11 codes. The training experience was evaluated using qualitative interviews, a questionnaire and a coding quiz.</p><p><strong>Results: </strong>total of 3011 charts were coded using ICD-11. In general, clinical coders provided positive feedback regarding the training program. The average score for the coding quiz (multiple choice, True/False) was 84%, suggesting that the training program was effective. Feedback from the coders enabled the ICD-11 code content, electronic tooling and terminologies to be updated.</p><p><strong>Conclusion: </strong>This study provides a detailed account of the processes involved with training clinical coders to use ICD-11. Important findings from the interviews were reported at the annual WHO conferences, and these findings helped improve the ICD-11 browser and reference guide.</p>","PeriodicalId":73210,"journal":{"name":"Health information management : journal of the Health Information Management Association of Australia","volume":"52 2","pages":"92-100"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/82/9f/10.1177_18333583211038633.PMC10170554.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9821849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transition from ICD-9-CM to ICD-10-CM/PCS in Portugal: An heterogeneous implementation with potential data implications.","authors":"João Vasco Santos,&nbsp;Ricardo Novo,&nbsp;Júlio Souza,&nbsp;Fernando Lopes,&nbsp;Alberto Freitas","doi":"10.1177/18333583211027241","DOIUrl":"https://doi.org/10.1177/18333583211027241","url":null,"abstract":"Dear Editor, In 2013, the planning process to implement the International Classification of Diseases – 10th revision – Clinical Modification/Procedure Coding System (ICD-10-CM/ PCS) for clinical coding in all public hospitals in Portugal began, replacing its predecessor, the International Classification of Diseases – 9th revision – Clinical Modification (ICD-9-CM), after almost three decades in use (Diário da República, 2013). The main rationale behind this transition is to improve specificity as the ICD-9-CM might fail to adequately represent all the current diversity of diagnoses and procedures (ACSS, 2016a; Lopes, 2012; Watzlaf et al., 2015). Moreover, the use of ICD-10-CM/PCS would allow an adaptation to newer definitions of the Diagnosis-Related Groups (DRG) software, which determines a case mix system for hospital reimbursement in Portugal and several other countries. The transition process to ICD-10-CM/PCS started in Portugal in August 2016, with three public hospitals being selected as pilots to implement the new classification system in October 2016, while the remaining hospitals would shift to ICD-10-CM/PCS by January 2017. In Portugal, clinical coding is done by trained medical coders based on information concerning patient disease progress, procedures and treatments that are routinely reported in health records, discharge notes, as well as pathology and surgical reports (ACSS, 2016b, 2021). Following the clinical coding of episodes, an audit phase is internally performed in hospitals usually by auditing clinicians (Barros and Braun, 2017). These audit processes are quite heterogeneous and not systematically performed in a hospital setting. In fact, despite some internal reviews takingplace, some medical coders have stated the need for relevant information for health records audits, namely, clearly defined audit processes, widely accepted official guidelines and health records standards (Alonso et al., 2020a). The resulting coded data concerning all inpatient, surgical or day hospital outpatient episodes from all hospitals within the Portuguese National Health Service (NHS) are gathered and held at the Hospital National Morbidity Database, managed by the Central Authority for Health Services, I.P. (ACSS). Despite its main purpose of hospital reimbursement, this comprehensive database is reused in several subdomains, ranging from clinical or health services research, to management and epidemiological studies, by either national or international reusers. During the transition process to ICD-10-CM/PCS, clinical coders’ training played a critical role. However, there are still sources of heterogeneity among hospitals regarding clinical coding practices that also impacted this transition, as resources, documents and tools used and abstracted during the clinical coding process (either electronically or on paper) differ considerably according to the hospital (Alonso et al., 2020a, 2020b). This process is also highly dependent on the informati","PeriodicalId":73210,"journal":{"name":"Health information management : journal of the Health Information Management Association of Australia","volume":"52 2","pages":"128-131"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10145083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}