BACKGROUND AND SIGNIFICANCE
diabetes, renal insufficiency
DECAY, RENAL INSUFFICIENCY, HYPERTENSION, DISEASES, DIABETES
WeedA complete patient problem list is the cornerstone of Dr Weed’s vision of the problem-oriented medical record. It serves as a valuable tool for providers assessing a patient’s clinical status and succinctly communicates this information between providers. An accurate problem list supports problem-oriented charting and can also help guide the flow of a clinical encounter, by reminding providers about important health issues to discuss or evaluate during the visit.Complete problem lists are also important for high-quality clinical decision support (CDS). Considerable evidence exists that, when effectively designed, CDS tools can improve quality of care and patient outcomes.Problem lists are also often used for clinical research and quality measurement. Many clinical improvement and research investigations, including large genomic studies, are becoming increasingly dependent on EHR data collected during clinical care.Further, the problem list is often used for quality measurements in EHRs, including those in the CMS meaningful use/promoting interoperability incentive program.An accurate problem list has been associated with higher quality care in observational studies. For example, in 2005, Hartung et alDespite this importance, coded problem lists are often inaccurate, incomplete, cluttered, and out of date. In previous work, we showed that problem list completeness in 1 network ranged from 4.7% for renal insufficiency or failure to 50.7% for hypertension, 61.9% for diabetes,Problem list completeness and use also varies dramatically by institution. For a single problem (diabetes), we found that the proportion of patients who had diabetes based on laboratory criteria had diabetes on their problem list 60.2% of the time at the lowest performing of 10 institutions, and 99.4% of the time at the highest performing institution.The causes of problem list incompleteness are myriad. In prior ethnographic work, we observed and interviewed 63 clinicians, and noted a “tragedy of the commons” occurring in many practice settings—providers reported that, frustrated with their incompleteness, they had stopped updating patient problem lists—this disuse then contributed to further decay of the problem list, causing other providers to also discontinue use.To improve problem list completeness, in prior work, we developed a series of algorithms which can identify missing problems from patient problem lists by analyzing other data in a patient’s EHR, including medications, laboratory results, and billing diagnoses. For example, if we detected a patient with a HbA1c of 9.2% who was on metformin, we inferred that they have diabetes and, if diabetes is missing from their problem list, we alerted their healthcare provider through the EHR. In a single-site randomized trial, we showed a 300% increase in problem list additions for the 17 conditions for which we had developed algorithms.Based on this prior work, we formulated 2 hypotheses: first, that the results from our single-site study could transfer to additional diseases and institutions—with CDS alerts leading to increased completeness of the problem list. Second, we further hypothesized that the CDS alerts would yield improvements in clinical quality, measured using EHR-based quality measures.