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

Background: In Portugal, trained physicians undertake the clinical coding process, which serves as the basis for hospital reimbursement systems. In 2017, the classification version used for coding of diagnoses and procedures for hospital morbidity changed from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS).

Objective: To assess the perceptions of medical coders on the transition of the clinical coding process from ICD-9-CM to ICD-10-CM/PCS in terms of its impact on data quality, as well as the major differences, advantages, and problems they faced.

Method: We conducted an observational study using a web-based survey submitted to medical coders in Portugal. Survey questions were based on a literature review and from previous focus group studies.

Results: A total of 103 responses were obtained from medical coders with experience in the two versions of the classification system (i.e. ICD-9-CM and ICD-10-CM/PCS). Of these, 82 (79.6%) medical coders preferred the latest version and 76 (73.8%) considered that ICD-10-CM/PCS guaranteed higher quality of the coded data. However, more than half of the respondents (N = 61; 59.2%) believed that more time for the coding process for each episode was needed.

Conclusion: Quality of clinical coded data is one of the major priorities that must be ensured. According to the medical coders, the use of ICD-10-CM/PCS appeared to achieve higher quality coded data, but also increased the effort.

Implications: According to medical coders, the change off classification systems should improve the quality of coded data. Nevertheless, the extra time invested in this process might also pose a problem in the future.

Background: Electronic medical records (EMRs) have been widely implemented in Australian hospitals. Their usability and design to support clinicians to effectively deliver and document care is essential, as is their impact on clinical workflow, safety and quality, communication, and collaboration across health systems. Perceptions of, and data about, usability of EMRs implemented in Australian hospitals are key to successful adoption.

Objective: To explore perspectives of medical and nursing clinicians on EMR usability utilising free-text data collected in a survey.

Method: Qualitative analysis of one free-text optional question included in a web-based survey. Respondents included medical and nursing/midwifery professionals in Australian hospitals (85 doctors and 27 nurses), who commented on the usability of the main EMR used.

Results: Themes identified related to the status of EMR implementation, system design, human factors, safety and risk, system response time, and stability, alerts, and supporting the collaboration between healthcare sectors. Positive factors included ability to view information from any location; ease of medication documentation; and capacity to access diagnostic test results. Usability concerns included lack of intuitiveness; complexity; difficulties communicating with primary and other care sectors; and time taken to perform clinical tasks.

Conclusion: If the benefits of EMRs are to be realised, there are good reasons to address the usability challenges identified by clinicians. Easy solutions that could improve the usability experience of hospital-based clinicians include resolving sign-on issues, use of templates, and more intelligent alerts and warnings to avoid errors.

Implications: These essential improvements to the usability of the EMR, which are the foundation of the digital health system, will enable hospital clinicians to deliver safer and more effective health care.

Background: Services data are an important source of information for policymakers and planners. In Australia, significant work has been undertaken to develop and implement collections of mental health services data. Given this level of investment, it is important that collected data are fit for purpose. Objective: This study aimed to: (1) identify existing national mandated and best endeavours collections of mental health services activity (e.g. occasions of service) and capacity (e.g. full-time equivalent staff) data in Australia; and (2) review the content of identified data collections to determine opportunities for data development. Method: A grey literature search was conducted to identify data collections. Where available, metadata and/or data were analysed. Results: Twenty data collections were identified. For services that received funding via multiple funding streams, data were often captured across several collections corresponding with each funder. There was significant variability in the content and format of collections. Unlike other service sectors, there is no national, mandated collection for psychosocial support services. Some collections have limited utility as they do not include key activity data; others do not include descriptive variables like service type. Workforce data are often not collected, and where data are collected, they are often not comprehensive. Conclusion: Findings are an important source of information for policymakers and planners who use services data to inform priorities. Implications: This study provides recommendations for data development, including mandating standardised reporting for psychosocial supports, filling workforce data gaps, streamlining data collections and including key missing data items in some collections.

Background: Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task.

Objective: This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena.

Method: We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011-2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding.

Results: We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding.

Discussion: We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness.

Conclusion: Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems.

Background: Patient online access to medical records is assumed to foster patient empowerment and advance patient-centred healthcare. Since July 2020, patients in the Netherlands have been legally entitled to electronically access their medical record in general practice. Experience from pioneering countries has shown that despite high patient interest, user rates often remain low. How to best support implementation depends on individual needs and expectations of patient populations, which are as yet unknown in the Dutch context.

Objective: To understand Dutch patients' needs and expectations with regard to online access to their medical record in general practice.

Method: Twenty participants completed semi-structured individual interviews via video or telephone call. Transcripts of interviews underwent template analysis combining deductive and inductive coding using Atlas.ti software.

Results: Patients' needs and expectations ranged across three overlapping areas: (i) prerequisites for getting online access; (ii) using online access; and (iii) the impact on interaction with healthcare providers. Patients expected benefits from online access such as better overview, empowerment and improved communication with their general practitioner but identified needs regarding technological difficulties, data privacy and complex medical language in their record.

Conclusion: The concerns and obstacles participants identified point towards the need for organisational changes in general practice, for example, adjusted documentation practices, and the key role of the general practitioner and staff in promoting and facilitating online access.

Implications: Implementation strategies addressing needs identified in this study may help to unlock the full potential of online access to achieve desired outcomes of patient involvement and satisfaction.

Background: Clinician notes are structured in a variety of ways. This research pilot tested an innovative study design and explored the impact of note formats on diagnostic accuracy and documentation review time.

Objective: To compare two formats for clinical documentation (narrative format vs. list of findings) on clinician diagnostic accuracy and documentation review time.

Method: Participants diagnosed written clinical cases, half in narrative format, and half in list format. Diagnostic accuracy (defined as including correct case diagnosis among top three diagnoses) and time spent processing the case scenario were measured for each format. Generalised linear mixed regression models and bias-corrected bootstrap percentile confidence intervals for mean paired differences were used to analyse the primary research questions.

Results: Odds of correctly diagnosing list format notes were 26% greater than with narrative notes. However, there is insufficient evidence that this difference is significant (75% CI 0.8-1.99). On average the list format notes required 85.6 more seconds to process and arrive at a diagnosis compared to narrative notes (95% CI -162.3, -2.77). Of cases where participants included the correct diagnosis, on average the list format notes required 94.17 more seconds compared to narrative notes (75% CI -195.9, -8.83).

Conclusion: This study offers note format considerations for those interested in improving clinical documentation and suggests directions for future research. Balancing the priority of clinician preference with value of structured data may be necessary.

Implications: This study provides a method and suggestive results for further investigation in usability of electronic documentation formats.

Background: Electronic health records (EHR) are seen as a promising endeavour, in spite of policies, designs, user rights and types of health data varying across countries. In many European countries, including Austria, EHR usage has fallen short when compared to the deployment plans.

Objective: By adopting a qualitative approach, this research aimed to explore facilitators and barriers experienced by patients and physicians across the entire EHR usage process in Austria.

Method: Two studies were conducted: In Study 1, discussions were held with four homogeneously composed groups of patients (N = 30). In Study 2, eight expert semi-structured interviews were conducted with physicians to gain insights into potential facilitators and barriers Austrian physicians face when utilising personal EHR.

Results: A wide range of barriers and facilitators were identified along the entire EHR usage spectrum, emerging on three different levels: the micro-level (individual level), the meso-level (level of the EHR system) and the macro-level (level of the health system). EHR literacy was identified as a booster to support EHR adherence. Health providers were identified as crucial gatekeepers with regard to EHR usage.

Conclusion: The implications for mutual benefits arising out of EHR usage among the triad of health policymakers, providers and patients for both theory and practice are discussed.

Background: This article reports on a study that investigated data professionals in health care. The topic is interesting and relevant because of the ongoing trend towards digitisation of the healthcare domain and efforts for it to become data driven, which entail a wide variety of work with data.

Objective: Despite an interest in data science and more broadly in data work, we know surprisingly little about the people who work with data in healthcare. Therefore, we investigated data work at a large national healthcare data organisation in Denmark.

Method: An explorative mixed method approach combining a non-probability technique for design of an open survey with a target population of 300+ and 11 semi-structured interviews, was applied.

Results: We report findings relevant to educational background, work identity, work tasks, and how staff acquired competences and knowledge, as well as what these attributes comprised. We found recurring themes of healthcare knowledge, data analytical skills, and information technology, reflected in education, competences and knowledge. However, there was considerable variation within and beyond those themes, and indeed most competences were learned "on the job" rather than as part of formal education.

Conclusion: Becoming a professional working with data in health care can be the result of different career paths. The most recurring work identity was that of "data analyst"; however, a wide variety of responses indicated that a stable data worker identity has not yet developed.

Implications: The findings present implications for educational policy makers and healthcare managers.

Background: Healthcare data frequently lack the specificity level needed to achieve clinical and operational objectives such as optimising bed management. Pneumonia is a disease of importance as it accounts for more bed days than any other lung disease and has a varied aetiology. The condition has a range of SNOMED CT concepts with different levels of specificity.

Objective: This study aimed to quantify the importance of the specificity of an SNOMED CT concept, against well-established predictors, for forecasting length of stay for pneumonia patients.

Method: A retrospective data analysis was conducted of pneumonia admissions to a tertiary hospital between 2011 and 2021. For inclusion, the primary diagnosis was a subtype of bacterial or viral pneumonia, as identified by SNOMED CT concepts. Three linear mixed models were constructed. Model One included known predictors of length of stay. Model Two included the predictors in Model One and SNOMED CT concepts of lower specificity. Model Three included the Model Two predictors and the concepts with higher specificity. Model performances were compared.

Results: Sex, ethnicity, deprivation rank and Charlson Comorbidity Index scores (age-adjusted) were meaningful predictors of length of stay in all models. Inclusion of lower specificity SNOMED CT concepts did not significantly improve performance (ΔR² = 0.41%, p = .058). SNOMED CT concepts with higher specificity explained more variance than each of the individual predictors (ΔR² = 4.31%, p < .001).

Conclusion: SNOMED CT concepts with higher specificity explained more variance in length of stay than a range of well-studied predictors.

Implications: Accurate and specific clinical documentation using SNOMED CT can improve predictive modelling and the generation of actionable insights. Resources should be dedicated to optimising and assuring clinical documentation quality at the point of recording.

Background: The health and well-being of children in the first 2000 days has a lasting effect on educational achievement and long-term chronic disease in later life. However, the lack of integration between high-quality data, analytic capacity and timely health improvement initiatives means practitioners, service leaders and policymakers cannot use data effectively to plan and evaluate early intervention services and monitor high-level health outcomes.

Objective: Our exploratory study aimed to develop an in-depth understanding of the system and clinical requirements of a state-wide paediatric learning health system (LHS) that uses routinely collected data to not only identify where the inequities and variation in care are, but also to also inform service development and delivery where it is needed most.

Method: Our approach included reviewing exemplars of how administrative data are used in Australia; consulting with clinical, policy and data stakeholders to determine their needs for a child health LHS; mapping the existing data points collected across the first 2000 days of a child's life and geospatially locating patterns of key indicators for child health needs.

Results: Our study identified the indicators that are available and accessible to inform service delivery and demonstrated the potential of using routinely collected administrative data to identify the gap between health needs and service availability.

Conclusion: We recommend improving data collection, accessibility and integration to establish a state-wide LHS, whereby there is a streamlined process for data cleaning, analysis and visualisation to help identify populations in need in a timely manner.