Accuracy of medical billing data against the electronic health record in the measurement of colorectal cancer screening rates.

ObjectiveMedical billing data are an attractive source of secondary analysis because of their ease of use and potential to answer population-health questions with statistical power. Although these datasets have known susceptibilities to biases, the degree to which they can distort the assessment of quality measures such as colorectal cancer screening rates are not widely appreciated, nor are their causes and possible solutions.MethodsUsing a billing code database derived from our institution's electronic health records, we estimated the colorectal cancer screening rate of average-risk patients aged 50-74 years seen in primary care or gastroenterology clinic in 2016-2017. 200 records (150 unscreened, 50 screened) were sampled to quantify the accuracy against manual review.ResultsOut of 4611 patients, an analysis of billing data suggested a 61% screening rate, an estimate that matches the estimate by the Centers for Disease Control. Manual review revealed a positive predictive value of 96% (86%-100%), negative predictive value of 21% (15%-29%) and a corrected screening rate of 85% (81%-90%). Most false negatives occurred due to examinations performed outside the scope of the database-both within and outside of our institution-but 21% of false negatives fell within the database's scope. False positives occurred due to incomplete examinations and inadequate bowel preparation. Reasons for screening failure include ordered but incomplete examinations (48%), lack of or incorrect documentation by primary care (29%) including incorrect screening intervals (13%) and patients declining screening (13%).ConclusionsBilling databases are prone to substantial bias that may go undetected even in the presence of confirmatory external estimates. Caution is recommended when performing population-level inference from these data. We propose several solutions to improve the use of these data for the assessment of healthcare quality

Improving health and public safety through knowledge management

This paper reports on KM in public healthcare and public safety. It reflects the experiences of the author as a CIO (Chief Information Officer) in both industries in Australia and New Zealand. There are commonalities in goals and challenges in KM in both industries. In the case of public safety a goal of modern policing theory is to move more towards intelligence-driven practice. That means interventions based upon research and analysis of information. In healthcare the goals include investment in capacity based upon knowledge of healthcare needs, evidence-based service planning and care delivery, capture of information and provision of knowledge at the point-of-care and evaluation of outcomes. The issue of knowledge management is explored from the perspectives of the user of information and from the discipline of Information Technology and its application to healthcare and public safety. Case studies are discussed to illustrate knowledge management and limiting or enabling factors. These factors include strategy, architecture, standards, feed-back loops, training, quality processes, and social factors such as expectations, ownership of systems and politics

Electronic health records to facilitate clinical research

Electronic health records (EHRs) provide opportunities to enhance patient care, embed performance measures in clinical practice, and facilitate clinical research. Concerns have been raised about the increasing recruitment challenges in trials, burdensome and obtrusive data collection, and uncertain generalizability of the results. Leveraging electronic health records to counterbalance these trends is an area of intense interest. The initial applications of electronic health records, as the primary data source is envisioned for observational studies, embedded pragmatic or post-marketing registry-based randomized studies, or comparative effectiveness studies. Advancing this approach to randomized clinical trials, electronic health records may potentially be used to assess study feasibility, to facilitate patient recruitment, and streamline data collection at baseline and follow-up. Ensuring data security and privacy, overcoming the challenges associated with linking diverse systems and maintaining infrastructure for repeat use of high quality data, are some of the challenges associated with using electronic health records in clinical research. Collaboration between academia, industry, regulatory bodies, policy makers, patients, and electronic health record vendors is critical for the greater use of electronic health records in clinical research. This manuscript identifies the key steps required to advance the role of electronic health records in cardiovascular clinical research

Tensions and paradoxes in electronic patient record research: a systematic literature review using the meta-narrative method

Background: The extensive and rapidly expanding research literature on electronic patient records (EPRs) presents challenges to systematic reviewers. This literature is heterogeneous and at times conflicting, not least because it covers multiple research traditions with different underlying philosophical assumptions and methodological approaches. Aim: To map, interpret and critique the range of concepts, theories, methods and empirical findings on EPRs, with a particular emphasis on the implementation and use of EPR systems. Method: Using the meta-narrative method of systematic review, and applying search strategies that took us beyond the Medline-indexed literature, we identified over 500 full-text sources. We used ‘conflicting’ findings to address higher-order questions about how the EPR and its implementation were differently conceptualised and studied by different communities of researchers. Main findings: Our final synthesis included 24 previous systematic reviews and 94 additional primary studies, most of the latter from outside the biomedical literature. A number of tensions were evident, particularly in relation to: [1] the EPR (‘container’ or ‘itinerary’); [2] the EPR user (‘information-processer’ or ‘member of socio-technical network’); [3] organizational context (‘the setting within which the EPR is implemented’ or ‘the EPR-in-use’); [4] clinical work (‘decision-making’ or ‘situated practice’); [5] the process of change (‘the logic of determinism’ or ‘the logic of opposition’); [6] implementation success (‘objectively defined’ or ‘socially negotiated’); and [7] complexity and scale (‘the bigger the better’ or ‘small is beautiful’). Findings suggest that integration of EPRs will always require human work to re-contextualize knowledge for different uses; that whilst secondary work (audit, research, billing) may be made more efficient by the EPR, primary clinical work may be made less efficient; that paper, far from being technologically obsolete, currently offers greater ecological flexibility than most forms of electronic record; and that smaller systems may sometimes be more efficient and effective than larger ones. Conclusions: The tensions and paradoxes revealed in this study extend and challenge previous reviews and suggest that the evidence base for some EPR programs is more limited than is often assumed. We offer this paper as a preliminary contribution to a much-needed debate on this evidence and its implications, and suggest avenues for new research

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research