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

Background: Work-integrated learning (WIL) is integral to most health disciplines' profession-qualifying degree programs. Objectives: To analyse the categories, locales and foci of final-year (capstone), health information management professional practice (WIL) placements, 2012-2021, at La Trobe University, Australia. Method: A documentary analysis of 614 placement agency proposals, 2012-2021, interrogated multiple characteristics: agency type, placement (sub-) category (WIL model), project type, agency-required student capabilities, intended learning outcomes. Results: Public hospitals offered 50% of all placements. Medical research/health or disease screening/clinical registries offered 17.8%, incorporating 86.7% of "research-based" placements. Government department offerings were consistently stable; private hospital, primary care and community healthcare offerings declined. The majority (64.8%) of offerings were "project-based," followed by "internship" (28.7%: Health Information Service (14%) and "other" (14.7%)), research-based (4.9%) and other (1.6%). Ninety-nine (16.1%) proposals specified additional, pre-placement skills and capabilities: technical (information technologies, software applications; 58.6% of 99 proposals); working independently (49.5%); communications (written, verbal; 45.5%); targeted interest (38.4%) in "informatics and data quality," "quality and safety," "software development," "coding"; organisational and/or time management skills (29.9%); teamwork skills (20.2%); data analysis skills (18.2%); enthusiasm and/or self-motivation (15.2%). Conclusion: The project-based model for the capstone placement is ideal for preparing health information management students for complex, graduate professional work. Agencies' pre-placement expectations of students (knowledge, technical skills, soft skills) are consistent with findings from the WIL literature and align with course curricula and Australia's Health Information Manager (HIM) Profession-entry Competency Standards. Implications: The findings will strengthen the health information management profession's knowledge base of WIL and inform educators, students and agency supervisors.

Background: Clinical coding is important for reimbursement, resource planning, administration and medical research. Objective: This study aimed to evaluate clinical coding accuracy and its influencing factors, especially the benefits of physician-clinical coder collaboration. Method: Twenty-four physicians and one senior clinical coder participated in the quality audit. The audit checklist, assessment criteria, training program and physician-clinical coder collaboration mechanism were clearly defined. The homepage filling standards, homepage filling guidelines and the guidelines of the International Classification of Diseases were used as the assessment criteria for evaluating accuracy. Results: A total of 323,320 medical records were reviewed. The average accuracy of homepage completion was 60.4% and poor-quality homepages accounted for 89.9% of coding errors. The average coding accuracy and correction percentage were 83.4% and 62.3%, respectively. After physician-clinical coder collaboration, the coding accuracy increased from 78.9% to 87.1% (χ² = 799.904, p < 0.001) and correction percentage increased from 52.0% to 73.0% (χ² = 1628.015, p < 0.001). Multivariate logistic regression revealed that complexity of medical records (odds ratio (OR) = 0.625), quality of homepages (OR = 20.445), month of physician-clinical coder collaboration (OR = 1.133-2.297), coder's major (OR = 1.616), coding experience (OR = 1.953), work engagement (OR = 1.290) and day of the week (OR = 1.054) were factors influencing coding accuracy. Conclusion: Physician-clinical coder collaboration effectively improved clinical coding accuracy and clinical coders benefited greatly. However, homepage quality was not improved. Furthermore, homepage quality and psychological factors influenced coding accuracy. Implications: Managers should implement regular standardised training for homepage completion, alongside ongoing improvements in coding practices and training.

Background: The COVID-19 pandemic has highlighted the critical role of mobile health applications in the management of health crises. Despite the promising outcomes of these technologies, however, their acceptance and use among physicians in the developing world such as Saudi Arabia are notably low. Objective: The study aimed to explore the factors influencing the acceptance and adoption of mobile health applications by physicians in Saudi Arabia during the COVID-19 pandemic. Method: The study employed a qualitative research method, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT). The study collected data through semi-structured interviews with 16 physicians to delve into the determinants of their readiness to adopt m-health technologies. Data were analysed using template analysis to identify key themes and patterns. Results: In line with the UTAUT, the study identified performance expectancy, effort expectancy, social influence and facilitating conditions as significant influencing factors of the acceptance and adoption of mobile health applications by physicians in Saudi Arabia during the pandemic. This study also inquired into context-specific determinants, such as data privacy concerns, patient engagement, organisational support and compatibility with religious and cultural norms, which are especially relevant in Saudi Arabia and similar developing countries, where these factors, alongside the exigencies arising from the COVID-19 pandemic, have shaped the landscape of mobile health applications utilisation. Conclusions: This study enriches the literature by expanding the UTAUT model to include context-specific drivers of acceptance and adoption. It highlights the need for tailored adoption frameworks to fit local contexts for successful m-health integration. Implications: This research broadens the UTAUT model by including cultural compatibility and data privacy concerns, offering deeper insights into mHealth adoption during crises. It highlights the need for policies and practices that support culturally sensitive app design, strengthen data privacy measures and provide improved training and patient engagement to enhance mHealth adoption.

Background: The burden of disease of Aboriginal and Torres Strait Islander people is estimated as 2.3 times that of the broader Australian population, with between 30% and 50% of health inequalities attributable to poor environmental health. Objective: Although many Australian states and territories have clinical policy initiatives that seek to reduce the burden of preventable disease in this population, including field-based environmental health clinical referrals (EHCRs), there is little consistency across the jurisdictions, resulting in less potential to break the cycle of recurrent diseases within the home environment. Method and Results: This study addresses this inconsistency by recommending recognition and categorisation of environmental health risks to allow for accurate diagnosis and comparability across health services and locations by using the International Statistical Classification of Diseases and Related Health Problems (ICD) system, already in use in hospitals. Conclusion and Implications: Developing a list of mutually agreed environmental health attributable diseases for the EHCR process using assigned ICD-10-AM codes would influence the provision of primary care to include recognition of the impact of environmental health conditions and allow environmental health staff to provide a response and education at both community and household levels to break disease cycles.

Background: Advances in technology have increased the ease of reporting hospital incidents, resulting in large amounts of qualitative descriptive data. Health services have little experience analysing these data at scale to incorporate into routine reporting.

Objective: We aimed to explore the feasibility of applying a semi-automated content analysis (SACA) tool (Leximancer™) to qualitative descriptions of system-wide hospital incidents to provide insights into safety issues at all health service levels.

Method: Data from 1245 incidents reported across a network of hospitals in Australia were analysed using the SACA tool. Summaries were generated using a variety of techniques, including inductive and deductive approaches to extract key concepts in the data.

Results: The analysis was feasible and provided an actionable summary of the types of incidents reported in the data; the visual interface allowed users to explore the underlying text for a deeper understanding. Deductive analysis was utilised to explore specific areas of interest, and stratified analysis revealed more detailed concepts. The SACA tool was more efficient than manual processes; however, due to the context present in the incident descriptions, significant time, reading and subject matter expertise is still required to refine the analysis.

Conclusion: Semi-automated tools provide an opportunity for improving patient safety culture and practices by providing rapid content analysis of vast datasets that can be customised for specific organisational contexts and deployed at scale. Further research is required to assess usefulness with system users.

Implications: Qualitative data abound and system-wide analysis is essential to creating actionable insights.

Background: The transition of systems to the International Statistical Classification of Diseases 11th Version (ICD-11) allows access to comprehensive data that accurately portray the complexity of morbidity and mortality data in Malaysia.

Objective: To demonstrate Malaysia's experience in implementing ICD-11, from data collection to downstream data use applications.

Method and implementation: We describe improvements to existing data source systems and downstream data applications. For non-HIS and HIS (ICD-10) systems, data were manually entered into the health management information system equipped with ICD-11 or automatically mapped from ICD-10 to ICD-11. Following these system improvements, we collected and reported ICD-11 data from all hospitals nationwide, regardless of the individual systems' status in ICD-11 use.

Discussion: Lessons learnt related to legacy systems; ICD-11 releases and system updates; mapping; reporting; human resources and related applications.

Conclusion: With careful planning, standardisation of the collection and use of ICD-11 data can be accomplished with limited resources and in a complex environment with heterogeneous systems.

Implications: Use of ICD-11 data in downstream data applications improves data quality to answer specific business or research questions.

Introduction: As health information systems (HIS) become a critical part of patient care, it is crucial to build an effective education strategy that facilitates the adoption and sustained use of these systems. The COVID-19 pandemic (2019-2023) has contributed to the rapid shift in virtual education and training for healthcare staff.

Objective: We sought to evaluate the efficacy and long-term sustainability of virtual training for using a HIS by examining opportunities and challenges.

Method: An exploratory, multimethods study was conducted with staff who had taken part in a virtual HIS training program as part of the clinical transformation journey at a large academic health science center in Canada. The study was guided by the Accelerating the Learning Cycle framework. Data were collected through pre- and post-training surveys, as well as semi-structured interviews. An iterative, inductive, constant comparative analysis approach, outlined by Braun and Clarke, was taken to thematically analyse the data.

Results: Of the 33 participants in this study, 13 were educational champions, and 20 were end-users. The pre- and post-training surveys yielded a total of 1479 responses in both groups. Three prominent themes emerged from this study: (1) fostering dynamic facilitation techniques to cultivate an inclusive culture and adapt to diverse learning needs; (2) integrating practical learning activities that contribute to knowledge retention; and (3) ensuring training resources are accessible and consistent for an optimal training experience.

Conclusion: As HIS continue to be part of the transformation of the healthcare ecosystem, education is vital in preparing healthcare providers to perform their clinical tasks and effectively use these technologies. Findings from this study can be used to inform the development of virtual training that is inclusive and addresses the needs of care providers.

Background: An increasing number of people are exploring their genetic predisposition to many diseases, allowing them to make healthcare decisions with improved knowledge. Objectives: The aim of this study was to identify factors that influence individuals to consider genetic testing utilising a modified health belief model (HBM). Method: The authors tested the modified HBM using a convenience sample of individuals from across the United States after a pilot study was used to test the validity and reliability of the constructs. Using SmartPLS, the researchers determined that the modified HBM explains the decision-making process used to determine what influences individuals to consider genetic testing. Results: Results suggested that perceived susceptibility, perceived benefits, cues to action, self-efficacy, e-health literacy and normative belief all play a role in an individual's decision to test their genetics. Conclusion: By conducting genetic testing, individuals may benefit from knowing they are predisposed to certain cancers and other diseases. Yet, research results have indicated that most individuals are unaware of resources available online that will help them in understanding genetic test results and associated diseases. Implications: Since healthcare literacy is an issue reported by these individuals, health information management professionals are well qualified to support them in e-health literacy by assisting them to evaluate the trustworthiness of available resources, and to educate them about privacy rights regarding access to and protection of their genetic information.

Background: Current methods of categorising the International Statistical Classification of Diseases and Related Health Problems (ICD) have limitations when deciphering administrative data and monitoring healthcare outcomes. These include many-to-one relationships, non-linear sequencing, collinearity, and ambiguous miscellaneous (residual) codes. Objective: Describe novel methodology for clinically meaningful categorisation of 12th Edition of ICD Version 10 Australian modification (ICD-10-AM). Setting: State of Victoria (Australia), population of 6.6 million with over 3 million separations per annum. Method: Diagnosis codes from ICD-10-AM were aggregated into Clinical Diagnosis Group (CDG) sets according to clinical features and associated risk of in-hospital death and complications. Residual codes were excluded. Administrative data from July 2020 to June 2023 were interrogated to ascertain frequency of diagnoses captured by CDG sets. Results: 12,716 (87.9%) of 14,470 total ICD-10-AM codes were aggregated into 406 CDG sets; mean 32 (range 1-288) codes per set. One thousand seven hundred fifty-three (12.1%) were excluded (not allocated): 775 (5.4%) residual codes; 702 (4.9%) indicating reason for healthcare encounter; and 276 (1.9%) ill-defined clinical symptom codes. Over 36-months, 11.8 million separations were coded with 11,898 (82.2%) unique ICD-10-AM diagnoses, including 10,721 (90.1%) present in a CDG set. Of the 8571 (59.2%) codes associated with death or complications, 7813 (91.2%) were present in a CDG set. Conclusion: The CDG list provides a clinically meaningful method of categorisation and interrogating datasets based on ICD-10-AM and complements existing methods.