Characterizing outpatient problem list completeness and duplications in the electronic health record

Abstract

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Objective</jats:title> <jats:p>The study sought to characterize rates of problem list completeness and duplications in common chronic diseases and to identify any relationships that they may have with respect to disease type, demographics, and disease severity.</jats:p> </jats:sec> <jats:sec> <jats:title>Materials and Methods</jats:title> <jats:p>We performed a retrospective analysis of electronic health record data from Partners HealthCare. We selected 8 common chronic diseases and identified patients with each of those diseases. We then analyzed each patient’s problem list for completeness and duplications and also collected information regarding demographics and disease severity. Rates of completeness and duplications were calculated for each disease and compared according to disease type, demographics, and disease severity.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>A total of 327 695 unique patients and 383 404 problem list entries were identified. Problem list completeness varied from 72.9% in hypertension to 93.5% in asthma, whereas problem list duplications varied from 4.8% in hypertension to 28.2% in diabetes. There was a variable relationship between demographic factors and rates of completeness and duplication. Rates of completeness were positively correlated with disease severity for most diseases. Rates of duplication were consistently positively correlated with disease severity.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>Incompleteness and duplications are both important issues in problem lists. These issues vary widely across different diseases and can also be impacted by patient demographics and disease severity. Further studies are needed to investigate the effect of individual user behaviors and organizational policies on problem list utilization, which will aid the development of interventions that improve the utility of problem lists.</jats:p> </jats:sec>

Real-world Effect of Monoclonal Antibody Treatment in COVID-19 Patients in a Diverse Population in the United States

“…This can be burdensome and stresses the importance of widespread availability of testing. Evidence also suggests that patients with more significant or severe comorbidities are likely to have more complete health records [ 26 , 27 ]. This effect may have overrepresented patients with more severe chronic conditions in the untreated group based on the application of mAb eligibility criteria for inclusion in the analysis.…”

Clinical data sharing improves quality measurement and patient safety

“…Prior research has shown EHR problem list incompleteness rates between 10% and 60% for an individual health system. 18 , 19 , 23 For the 4 measures that included diagnoses as part of the measure calculation (ie, diabetes and hypertension measures), over 24% of measure calculations change when HIE data are included. Because data sharing through an HIE closes a portion of incomplete diagnoses before quality calculation, the magnitude of measure discrepancies observed in this research generally align with incompleteness expectations from other research.…”

Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis

“…Similarly, attempts to characterize endometriosis through data collection designs relying solely on EHR problem lists might yield incomplete or biased results, findings supported by previous studies conducting in other conditions. 14 16 90 From a clinical standpoint, pain variability has been demonstrated to be predictive of treatment response and disease outcomes. 79 91 For example, a study 79 reported that higher within-person pain variability at baseline in a randomized controlled trial for a fibromyalgia pain drug was predictive of placebo response and nonspecific treatment effects.…”

“…A recent study of EHR problem list completeness and accuracy reports that providers struggle more with maintaining complete problem lists for patients whose diseases status are less severe and less symptomatic. 16 Accordingly, some of the patients identified in the EHR cohort might have reported additional pain-related findings, but these might have not been documented by the provider.…”

“… 15 Health providers may not log diagnosis codes for observed conditions that do not require treatment, diagnostic testing, or referral. 8 Previous studies further report incompleteness and significant variability in provider agreement on problem lists, 16 and significant burnout due to EHR use among providers 17 18 19 resulting in less time-treating patients, incomplete chart notes, and duplicate records. 17 These factors can collectively contribute to deficiencies in EHR data accuracy, quality, and level of detail.…”