EGEMS (Washington, DC)
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EGEMS (Washington, DC) · Apr 2019
Impact of ICD-10-CM Transition on Mental Health Diagnoses Recording.
This study examines the impact of the transition from ICD-9-CM to ICD-10-CM diagnosis coding on the recording of mental health disorders in electronic health records (EHRs) and claims data in ten large health systems. We present rates of these diagnoses across two years spanning the October 2015 transition. ⋯ For most mental health conditions, the transition to ICD-10-CM appears to have had minimal impact. The decrease seen for psychosis diagnoses in these health systems is likely due to changes associated with EHR implementation of ICD-10-CM coding rather than an actual change in disease prevalence. It is important to consider the impact of the ICD-10-CM transition for all diagnostic criteria used in research studies, quality measurement, and financial analysis during this interval.
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EGEMS (Washington, DC) · Jan 2019
Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities.
Examples of fully integrated machine learning models that drive clinical care are rare. Despite major advances in the development of methodologies that outperform clinical experts and growing prominence of machine learning in mainstream medical literature, major challenges remain. At Duke Health, we are in our fourth year developing, piloting, and implementing machine learning technologies in clinical care. ⋯ Academic medical centers that cultivate and value transdisciplinary collaboration are ideally suited to integrate machine learning in clinical care. Along with fostering collaborative environments, health system leaders must invest in developing new capabilities within the workforce and technology infrastructure beyond standard electronic health records. Now is the opportunity to break down barriers and achieve scalable growth in the number of high-impact collaborations between clinical researchers and machine learning experts to transform clinical care.
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EGEMS (Washington, DC) · Dec 2017
Analytical Methods for a Learning Health System: 3. Analysis of Observational Studies.
The third paper in a series on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review describes how analytical methods for individual-level electronic health data EHD, including regression approaches, interrupted time series (ITS) analyses, instrumental variables, and propensity score methods, can also be used to address the question of whether the intervention "works." The two major potential sources of bias in non-experimental studies of health care interventions are that the treatment groups compared do not have the same probability of treatment or exposure and the potential for confounding by unmeasured covariates. Although very different, the approaches presented in this chapter are all based on assumptions about data, causal relationships, and biases. For instance, regression approaches assume that the relationship between the treatment, outcome, and other variables is properly specified, all of the variables are available for analysis (i.e., no unobserved confounders) and measured without error, and that the error term is independent and identically distributed. ⋯ To properly address these assumptions, analysts should conduct sensitivity analyses within the assumptions of each method to assess the potential impact of what cannot be observed. Researchers also should analyze the same data with different analytical approaches that make alternative assumptions, and to apply the same methods to different data sets. Finally, different analytical methods, each subject to different biases, should be used in combination and together with different designs, to limit the potential for bias in the final results.
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EGEMS (Washington, DC) · Jan 2016
Extracting Electronic Health Record Data in a Practice-Based Research Network: Processes to Support Translational Research across Diverse Practice Organizations.
The widespread adoption of electronic health records (EHRs) offers significant opportunities to conduct research with clinical data from patients outside traditional academic research settings. Because EHRs are designed primarily for clinical care and billing, significant challenges are inherent in the use of EHR data for clinical and translational research. Efficient processes are needed for translational researchers to overcome these challenges. The Data QUEST Coordinating Center (DQCC), which oversees Data Query Extraction Standardization Translation (Data QUEST) - a primary-care, EHR data-sharing infrastructure - created processes that guide EHR data extraction for clinical and translational research across these diverse practices. We describe these processes and their application in a case example. ⋯ The experience of the DQCC demonstrates that coordinating centers provide expertise in helping researchers understand the context of EHR data, create and maintain governance structures, and guide the definition of parameters for data extractions. This expertise is critical to supporting research with EHR data. Replication of these strategies through coordinating centers may lead to more efficient translational research. Investigators must also consider the impact of initial decisions in defining study groups that may potentially affect outcomes.
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EGEMS (Washington, DC) · Jan 2016
Using Machine Learning and Natural Language Processing Algorithms to Automate the Evaluation of Clinical Decision Support in Electronic Medical Record Systems.
As the number of clinical decision support systems (CDSSs) incorporated into electronic medical records (EMRs) increases, so does the need to evaluate their effectiveness. The use of medical record review and similar manual methods for evaluating decision rules is laborious and inefficient. The authors use machine learning and Natural Language Processing (NLP) algorithms to accurately evaluate a clinical decision support rule through an EMR system, and they compare it against manual evaluation. ⋯ CDSSs incorporated into EMRs can be evaluated in an automatic fashion by using NLP and machine learning techniques.