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

Background and objective: Hospital administrative data serves as a rich source of information for resource allocation, surveillance, and international comparisons. Differences in coding practices and guidelines lead to variations in hospital administrative data. This study outlines our team's process of creating a standardised tool, the Hospital Administrative Data Quality (HADQ) scoring tool, which will allow countries to assess their HADQ.

Method: We previously developed 24 indicators through a Delphi consensus method. To test the applicability of these indicators, we approached 50 countries with an online survey comprised of qualitative and quantitative questions based on the 24 indicators. An overall score out of 20 for data quality was calculated for each country based on the survey responses. The score was classified into three categories: high data quality, moderate data quality and low data quality.

Results: Of the 50 countries invited, 17 responded. Surveys from two countries were excluded due to insufficient data. Country responses were evaluated and scored by dimension. The data quality indicators showed positive face validity and were applicable for most countries providing comparative information for development of the tool with good discrimination. Canada, United States of America, New Zealand, United Kingdom, and Spain were among the countries with an overall high data quality score. Most countries scored high in 3 out of 5 dimensions of data quality. A few counties scored 0 in "Relevance" and "Timeliness" resulting in a lower score.ConclusionThe HADQ tool developed in this study will support the assessment and comparison of HADQ by applying the same standard within and across countries.Implications for health information management practice:The HADQ tool can be used by diverse users such as the researchers, government bodies and policymakers interested in improving hospital administrative data quality following standardised indicators that can applied globally.

Background: Hospital-acquired complications have detrimental effects on patient outcomes and recovery, with increased morbidity and mortality burdens, and hospital efficiency. The Australian Commission on Safety and Quality in Healthcare has identified 16 high-priority complications, including healthcare-associated infections, as potential targets of clinical risk mitigation strategies. Within the North Metropolitan Health Service in Western Australia, the prevalence of urinary tract infections (UTIs) was recognised as one of the most ubiquitous hospital-acquired complications and thus, there was desire to find new and innovative ways to enhance the existing infection prevention and control practices.

Objective: To develop a risk prediction model for early identification of inpatients at risk of acquiring a UTI, to support clinical processes to facilitate targeted intervention strategies.

Method: Prognostic modelling techniques were employed using retrospective hospital separation data encompassing patient and local health service factors.

Results: The risk prediction model, developed from approximately 350 variables, used just 9 factors: 2 patient characteristics (age, gender), 4 clinical factors (paraplegia, dementia, prostate hyperplasia, neurosurgeon care), and 3 process measures (hospital stay duration, long theatre time, intensive care unit stay). It predicted UTI risk with 91% sensitivity, 86% specificity, and 95% discrimination (area under the curve). Real-time use in ward settings suggested it could help reduce hospital-acquired urinary tract infections (HAUTIs).

Conclusion: Predictive modelling techniques can identify patients at risk of developing a HAUTI with high sensitivity and specificity. The resulting model can be used as a real-time clinical decision-making tool to guide proactive interventions and help reduce the prevalence of UTIs among hospital inpatients.Implications for health information management practice:The development and successful validation of a real-time predictive model for HAUTIs demonstrates how health information managers can leverage routinely collected data to support proactive clinical risk mitigation. Integrating such models into electronic health record systems can enhance patient safety, improve clinical workflows, and inform targeted infection control interventions across hospital settings.

Background: An effective strategy for promoting ethics within organisations is to establish clear guidelines that outline acceptable behaviours in different situations through a code of ethics. In the field of health information management (HIM), a code of ethics addresses various challenges, including privacy and information security. Objective: This study aimed to develop a code of ethics for health information management (HIM) professionals in Iran. Method: A mixed-methods study was conducted using a developmental approach. First, the code of ethics of the selected HIM and medical informatics associations was reviewed, and a comparative list of the codes of ethics prepared. An expert work group proposed the HIM code of ethics and then validated it using the Delphi technique. Results: After reviewing the code of ethics of the selected associations, we identified 70 statements. Following discussions and meetings, we prepared and approved 28 of these statements with input from 42 commentators using the Delphi technique. Notably, 46% of the code of ethics statements that aligned with respect for autonomy was deemed the most important principle. The principle of justice followed this at 32%, the principle of beneficence at 29%, and the principle of non-maleficence at 14%. Conclusion: Iran's current HIM laws and policies are inadequate. Developing a code of ethics could enhance patient rights, information preservation and collaboration with specialists. Implementing these codes of ethics nationally could promote ethical culture, transparency and quality. Establishing regulatory mechanisms, training for effective implementation and periodic revisions are important. Implications for health information practice: The proposed codes of ethics guide HIM professionals, enhancing public trust and assisting with ethical dilemmas as they manage systems integrating patient data, Artificial Intelligence, and Electronic Health Records.

Background: The validity of using hospital discharge abstract database (DAD) diagnostic codes to identify insulinoma remains unverified. Objective: The study aimed to develop case-finding algorithms to identify insulinomas using ICD-10 codes from hospital DAD, evaluate their performance and investigate causes of misidentification. Method: This study utilised a 12-year retrospective dataset from a large medical centre in China to test four ICD algorithms for identifying insulinoma patients with hospital DAD. Algorithm performance was evaluated against electronic medical records, using sensitivity, specificity and predictive values as metrics. Results: This study involved 4929 patients with pancreatic tumours, including 610 insulinoma patients, resulting in 5760 hospitalisations. Algorithm variant 1, which uses the code M8151, achieved a sensitivity of 69% and a specificity of 99.7%. The most comprehensive algorithm, variant 4, demonstrated a sensitivity of 84.8% and a specificity of 99.5%. Both algorithm variants 1 and 4 showed higher sensitivity in patients admitted to the endocrinology department and in those lacking pathological evidence (p < 0.001). The use of "pancreatic neuroendocrine tumour" instead of "insulinoma" in pathology reports and inadequate documentation of hypoglycaemia were the main contributors to coding inaccuracies. Conclusion: Owing to the complexity and variability of insulinoma documentation, a single morphology code was not ideal for identifying insulinoma in the hospital DAD. Enhanced, comprehensive algorithms are expected to better recognise confirmed cases. Implications for health information management practice: A multidisciplinary approach with improved pathology reporting, hypoglycaemia statements and precise tumour site-specific coding is crucial for improving the sensitivity of coded data in the hospital DAD for the identification of insulinoma.

Background: The World Health Organization endorsed the International Classification of Diseases 11th revision (ICD-11) in 2019 for ICD morbidity and mortality data reports. In Malaysia, the Ministry of Health (MOH) started to use it in 2024 after the long history of ICD-10 used since the 1990s. Objective: The present study aimed to evaluate the effectiveness of the ICD-11 training program conducted by the MOH. Method: A prospective single-arm study was conducted among 45 clinical coders at the MOH, where an online self-administered questionnaire included the (i) reaction level and (ii) learning level. Descriptive statistics were applied to the learning and reaction level input for data analysis. A paired t-test statistical analysis was used to analyse the difference in learning level scores in pre- and post-training programs. Results: Participants' overall satisfaction with the training courses was 93%, with most choosing somewhat agree and strongly agree in the reaction-level questionnaire. For the learning level, the average mean score for post-training (5.47) was higher when compared to pre-training scores (3.62), with a paired-sample test showing a significant difference (p < 0.05) between the mean scores. Conclusion: The increased efficacy in ICD-11 diagnosis coding among participant clinical coders post-training was statistically significant, and we recommend that MOH continue to host such training programs to enhance the knowledge and expertise of clinical coders. Implications for health information management practice: These findings emphasise the effectiveness of ICD-11 training programs, and add to the growing evidence about the need for standardised ICD training programs and resources to educate and train clinical coders in preparation for ICD-11 international rollout.

Background: Hospital discharge is a vulnerable point in a patient's journey where increased medication-related errors occur. Despite the importance of discharge summaries as a communication tool at the hospital-community healthcare interface, many factors impede doctors' ability to prepare accurate and timely discharge summaries and medication lists. Objective: To explore junior doctors' perceptions of the hospital discharge process. Method: Semi-structured interviews were conducted with purposively selected junior doctors (interns, residents and registrars) from a metropolitan health service between November and December 2022. The interview tool contained prompts to explore opinions and experiences relating to the discharge processes, specifically discharge summaries, medication reconciliation and collaboration between healthcare professionals. Interview transcriptions were thematically analysed. Results: Twenty junior doctors were interviewed. Five interconnected themes emerged: (1) workload and time restraints, (2) perceptions of medication reconciliation, (3) collaboration with pharmacists, (4) communication between healthcare professionals and (5) impact of organisational factors. Significant barriers to preparing discharge summaries included time pressures and high patient turnover. Factors aiding doctors included direct involvement in the patient's care, designated time to complete discharge summaries, well-documented in-patient progress notes and availability of comprehensive admission medication information recorded by pharmacists. Conclusion: Junior doctors face significant challenges during the hospital discharge process. Under heavy workloads and high patient turnover, continuity of care can be compromised as direct patient care tasks take priority over preparing discharge summaries. Implications for health information management practice: The findings underscore the need for enhanced medicines education and support for junior doctors, alongside improved integration of pharmacist expertise, to facilitate clear, accurate and timely discharge documentation.

Background: Although client information systems (CIS) should provide social welfare professionals (SWPs) with a comprehensive overview of a client's situation for case-based knowledge formation (CBKF), research into SWPs' user experiences is scarce. Objective: The aim of this study was to examine SWPs' experiences of CISs' support for CBKF. Methods: In 2020, a nationwide cross-sectional CIS usability survey was conducted with 980 respondents in Finland. The 16 questionnaire statements pertained to technical functionality, usability, client information quality and access to case-based information in CISs. The factors contributing to CISs' support for CBKF were analysed using univariable and multiple classification analyses. Results: The strongest predictors of CIS support for CBKF were usability and quality of information. Moreover, SWPs working in institutional care were more satisfied than their colleagues in other working environments. Conclusion: SWPs perceived the CISs' information quality to be good, but there was a need for improvement in usability, for instance, via comprehensive summary views and dashboards of essential information. Implications for health information management practice: The findings highlight that while the information quality of CISs is generally perceived as good, improving usability is crucial for enhancing support for CBKF among SWPs.

Background: Existing research has long established that direct exposure to patient trauma, such as severe injuries, chronic illnesses and end-of-life care, places clinical healthcare workers at heightened risk of secondary traumatic stress, compassion fatigue and burnout. However, comparatively little attention has been paid to the impact on non-clinical healthcare personnel, such as health information managers (HIMs) who, despite being removed from direct patient care, regularly handle distressing and sensitive patient information.

Objective: This scoping review explores the literature concerning non-clinical healthcare professionals and the potential impact upon their biopsychosocial-spiritual (BPSS) well-being given prolonged exposure to medical and/or patient records.

Method: Arksey and O'Malley's five-stage scoping review strategy was utilised. An initial search of the literature yielded no results specific to HIMs and other non-clinical healthcare professionals. Therefore, the scope of the review was broadened, and a second search of the literature was conducted to explore comparable non-patient/client-facing populations such as transcriptionists.

Results: In total 1226 articles were initially identified and 13 articles revealed either a biological, psychological, social and/or spiritual impact when professionals were exposed to traumatic and/or sensitive data.

Conclusion: Exploring the roles of comparable non-patient/client-facing populations provides insight into the potential impact that exposure to traumatic and/or sensitive information may have on the health and well-being of HIMs and other non-clinical health professionals.Implications for health information management practice:Further research is recommended to explore the potential BPSS impact that HIMs and other non-clinical health professionals experience due to the exposure of traumatic and/or sensitive information.

Background: The effective sharing of health data is critical for advancing public health research and improving health outcomes. Data custodians responsible for managing these data encounter various obstacles that prevent the efficient exchange of this information for research purposes. This study explores specific challenges to effective data custodianship and how they can be addressed to improve data sharing within the Australian health and human services sectors.

Objectives: A case study to explore the complex landscape of data sharing from the perspective of data custodians. This includes identifying legislative complexities and related organisational cultural challenges associated with data requests that accompany the role of a data custodian and suggesting strategies to facilitate data sharing.

Method: Utilising qualitative thematic analysis, semi-structured interviews were conducted to collect detailed perspectives on the practices and challenges of sharing health data from 11 data custodians across nine Australian healthcare organisations.

Results: The study highlighted several common challenges affecting data sharing for research, including legislative hurdles, lack of uniform standards for data access, and inconsistent consent protocols across datasets. Internal organisational factors, such as data request assessment processes, organisational culture, and attitudes towards data sharing, emerged as critical barriers to the efficient sharing of data.

Conclusion: Overcoming data-sharing barriers necessitates a multifaceted approach, requiring clear and consistent legislative frameworks for data access and establishing standards for transparent and efficient request assessment processes.Implications for health information management:Shifting data from a liability to a valuable asset can enhance decision-making, foster collaboration, and drive health sector innovation.

Background: Hospital-acquired complications (HACs) have an adverse impact on patient recovery by impeding their path to full recovery and increasing healthcare costs.

Objective: The aim of this study was to create a HAC risk prediction machine learning (ML) framework using hospital administrative data collections within North Metropolitan Health Service (NMHS), Western Australia.

Method: A retrospective cohort study was performed among 64,315 patients between July 2020 to June 2022 to develop an automated ML framework by inputting HAC and the healthcare site to obtain site-specific predictive algorithms for patients admitted to the hospital in NMHS. Univariate analysis was used for initial feature screening for 270 variables. Of these, 77 variables had significant relationship with any HAC. After excluding non-contemporaneous data, 37 variables were included in developing the ML framework based on logistic regression (LR), decision tree (DT) and random forest (RF) models to predict occurrence of four specific HACs: delirium, aspiration pneumonia, pneumonia and urinary tract infection.

Results: All models exhibited similar performance with area under the curve scores around 0.90 for both training and testing datasets. For sensitivity, DT and RF exceeded LR performance while on average, false positives were lowest for LR-based models. Patient's length of stay, Charlson Index, operation length and intensive care unit stay were common predictors.

Conclusion: Integrating ML-based risk detection systems into clinical workflows can potentially enhance patient safety and optimise resource allocation. LR-based models exhibited best performance.

Implications: We have successfully developed a "real-time" risk prediction model, where patient risk scores are calculated and reviewed daily.