Why Is the Electronic Health Record So Challenging for Research and Clinical Care?

Holmes, John H.; Beinlich, James; Boland, Mary Regina; Bowles, Kathryn H.; Chen, Yong; Cook, Tessa S.; Demiris, George; Draugelis, Michael; Fluharty, Laura; Gabriel, Peter; Grundmeier, Robert W.; Hanson, C. William; Herman, Daniel S.; Himes, Blanca E.; Hubbard, Rebecca A.; Kahn, Charles Η.; Kim, Dokyoon; Koppel, Ross; Long, Qi; Mirkovic, Nebojsa; Morris, Jeffrey S.; Mowery, Danielle L.; Ritchie, Marylyn D.; Urbanowicz, Ryan J.; Moore, Jason H.

doi:10.1055/s-0041-1731784

Cited by 34 publications

(35 citation statements)

References 84 publications

(77 reference statements)

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“… 118 149–153 Additional legal and social constraints limit access to sensitive sociodemographic data. 154 In Canada, for example, the collection of race/ethnicity data in healthcare settings has been restricted due to a range of historical and socio-political forces. For example, Thompson 155 illustrates how the Holocaust in the Second World War shook the foundations of the biological construction of race, which raised serious questions about the ethics of collecting this data.…”

Section: Resultsmentioning

confidence: 99%

Conceptualising fairness: three pillars for medical algorithms and health equity

Sikstrom

Maslej

Hui

et al. 2022

BMJ Health Care Inform

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ObjectivesFairness is a core concept meant to grapple with different forms of discrimination and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet, claims to fairness in ML discourses are often vague and contradictory. The response to these issues within the scientific community has been technocratic. Studies either measure (mathematically) competing definitions of fairness, and/or recommend a range of governance tools (eg, fairness checklists or guiding principles). To advance efforts to operationalise fairness in medicine, we synthesised a broad range of literature.MethodsWe conducted an environmental scan of English language literature on fairness from 1960-July 31, 2021. Electronic databases Medline, PubMed and Google Scholar were searched, supplemented by additional hand searches. Data from 213 selected publications were analysed using rapid framework analysis. Search and analysis were completed in two rounds: to explore previously identified issues (a priori), as well as those emerging from the analysis (de novo).ResultsOur synthesis identified ‘Three Pillars for Fairness’: transparency, impartiality and inclusion. We draw on these insights to propose a multidimensional conceptual framework to guide empirical research on the operationalisation of fairness in healthcare.DiscussionWe apply the conceptual framework generated by our synthesis to risk assessment in psychiatry as a case study. We argue that any claim to fairness must reflect critical assessment and ongoing social and political deliberation around these three pillars with a range of stakeholders, including patients.ConclusionWe conclude by outlining areas for further research that would bolster ongoing commitments to fairness and health equity in healthcare.

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Section: Resultsmentioning

confidence: 99%

Conceptualising fairness: three pillars for medical algorithms and health equity

Sikstrom

Maslej

Hui

et al. 2022

BMJ Health Care Inform

View full text Add to dashboard Cite

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“…Such data remain challenging to obtain at scale, leading to renewed efforts to utilize the extant clinical infrastructure and electronic medical records to help emulate traditional longitudinal analyses. Electronic medical records can help provide such data, but challenges such as missingness, limited quality control, and potential biases in care 182 need to be resolved with carefully considered analytical designs. 183 Emerging treatments Two novel atypical antipsychotics, amilsupride and bifeprunox, are currently being tested in phase 3 trials (NCT05169710 and NCT00134459) and could gain approval for bipolar depression in the near future if these pivotal trials show a significant antidepressant effect.…”

Section: Treatment Considerations To Reduce Suicide In Bipolar Disordermentioning

confidence: 99%

Diagnosis and management of bipolar disorders

Goes

2023

BMJ

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Bipolar disorders (BDs) are recurrent and sometimes chronic disorders of mood that affect around 2% of the world’s population and encompass a spectrum between severe elevated and excitable mood states (mania) to the dysphoria, low energy, and despondency of depressive episodes. The illness commonly starts in young adults and is a leading cause of disability and premature mortality. The clinical manifestations of bipolar disorder can be markedly varied between and within individuals across their lifespan. Early diagnosis is challenging and misdiagnoses are frequent, potentially resulting in missed early intervention and increasing the risk of iatrogenic harm. Over 15 approved treatments exist for the various phases of bipolar disorder, but outcomes are often suboptimal owing to insufficient efficacy, side effects, or lack of availability. Lithium, the first approved treatment for bipolar disorder, continues to be the most effective drug overall, although full remission is only seen in a subset of patients. Newer atypical antipsychotics are increasingly being found to be effective in the treatment of bipolar depression; however, their long term tolerability and safety are uncertain. For many with bipolar disorder, combination therapy and adjunctive psychotherapy might be necessary to treat symptoms across different phases of illness. Several classes of medications exist for treating bipolar disorder but predicting which medication is likely to be most effective or tolerable is not yet possible. As pathophysiological insights into the causes of bipolar disorders are revealed, a new era of targeted treatments aimed at causal mechanisms, be they pharmacological or psychosocial, will hopefully be developed. For the time being, however, clinical judgment, shared decision making, and empirical follow-up remain essential elements of clinical care. This review provides an overview of the clinical features, diagnostic subtypes, and major treatment modalities available to treat people with bipolar disorder, highlighting recent advances and ongoing therapeutic challenges.

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“…EHRs capture an unparalleled breadth and depth of clinical, quality, process, and outcome measures [12][13][14][15]. However secondary EHR data use relies heavily on unstructured data and manual extraction which limit its practical use [16,17]. Automated structured and unstructured data capture is possible using EHR-based tools and algorithms [18].…”

Section: Introductionmentioning

confidence: 99%

Extracting patient-level data from the electronic health record: Expanding opportunities for health system research

Farrand

Collard²,

Guarnieri³

et al. 2023

PLoS ONE

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Background Epidemiological studies of interstitial lung disease (ILD) are limited by small numbers and tertiary care bias. Investigators have leveraged the widespread use of electronic health records (EHRs) to overcome these limitations, but struggle to extract patient-level, longitudinal clinical data needed to address many important research questions. We hypothesized that we could automate longitudinal ILD cohort development using the EHR of a large, community-based healthcare system. Study design and methods We applied a previously validated algorithm to the EHR of a community-based healthcare system to identify ILD cases between 2012–2020. We then extracted disease-specific characteristics and outcomes using fully automated data-extraction algorithms and natural language processing of selected free-text. Results We identified a community cohort of 5,399 ILD patients (prevalence = 118 per 100,000). Pulmonary function tests (71%) and serologies (54%) were commonly used in the diagnostic evaluation, whereas lung biopsy was rare (5%). IPF was the most common ILD diagnosis (n = 972, 18%). Prednisone was the most commonly prescribed medication (911, 17%). Nintedanib and pirfenidone were rarely prescribed (n = 305, 5%). ILD patients were high-utilizers of inpatient (40%/year hospitalized) and outpatient care (80%/year with pulmonary visit), with sustained utilization throughout the post-diagnosis study period. Discussion We demonstrated the feasibility of robustly characterizing a variety of patient-level utilization and health services outcomes in a community-based EHR cohort. This represents a substantial methodological improvement by alleviating traditional constraints on the accuracy and clinical resolution of such ILD cohorts; we believe this approach will make community-based ILD research more efficient, effective, and scalable.

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Why Is the Electronic Health Record So Challenging for Research and Clinical Care?

Cited by 34 publications

References 84 publications

Conceptualising fairness: three pillars for medical algorithms and health equity

Conceptualising fairness: three pillars for medical algorithms and health equity

Diagnosis and management of bipolar disorders

Extracting patient-level data from the electronic health record: Expanding opportunities for health system research

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