Improving communication about goals and values for patients with advancing serious illness nearing the end of life is a key opportunity to improve the value of care. The Serious Illness Care Program, implemented at primary care clinics affiliated with Brigham and Women's Hospital in Boston, Massachusetts, is a multicomponent intervention designed to support best practices in communication by clinicians to increase conversations with patients with serious illness about their goals and values. We conducted a study of the program in fourteen primary care clinics participating in a high-risk care management program based in an accountable care organization. Patients in the clinics with the program implemented were more likely than those in comparison clinics to have serious illness conversations-including discussion of values and goals-documented in patients' medical records. Clinicians who participated also reported high satisfaction with training they received as part of the program, which they regarded as effective. This work suggests that the Serious Illness Care Program promotes more and better conversations among selected primary care patients, and it highlights the need for further research.

Association rule mining appears to be a useful tool for identifying clinically accurate associations between medications, laboratory results and problems and has several important advantages over alternative knowledge-based approaches.

We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.

BackgroundThe clinical problem list is an important tool for clinical decision making, quality measurement and clinical decision support; however, problem lists are often incomplete and provider attitudes towards the problem list are poorly understood.MethodsAn ethnographic study of healthcare providers conducted from April 2009 to January 2010 was carried out among academic and community outpatient medical practices in the Greater Boston area across a wide range of medical and surgical specialties. Attitudes towards the problem list were then analyzed using grounded theory methods.ResultsAttitudes were variable, and dimensions of variations fit into nine themes: workflow, ownership and responsibility, relevance, uses, content, presentation, accuracy, alternatives, support/education and one cross-cutting theme of culture.ConclusionsSignificant variation was observed in clinician attitudes towards and use of the electronic patient problem list. Clearer guidance and best practices for problem list utilization are needed.

BackgroundAccurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are often incomplete or out of date.ObjectiveTo determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation.Study Design and MethodsInference rules for 17 conditions were constructed and an electronic health record-based intervention was evaluated to improve problem documentation. A cluster randomized trial was conducted of 11 participating clinics affiliated with a large academic medical center, totaling 28 primary care clinical areas, with 14 receiving the intervention and 14 as controls. The intervention was a clinical alert directed to the provider that suggested adding a problem to the electronic problem list based on inference rules. The primary outcome measure was acceptance of the alert. The number of study problems added in each arm as a pre-specified secondary outcome was also assessed. Data were collected during 6-month pre-intervention (11/2009–5/2010) and intervention (5/2010–11/2010) periods.Results17 043 alerts were presented, of which 41.1% were accepted. In the intervention arm, providers documented significantly more study problems (adjusted OR=3.4, p<0.001), with an absolute difference of 6277 additional problems. In the intervention group, 70.4% of all study problems were added via the problem list alerts. Significant increases in problem notation were observed for 13 of 17 conditions.ConclusionProblem inference alerts significantly increase notation of important patient problems in primary care, which in turn has the potential to facilitate quality improvement.Trial RegistrationClinicalTrials.gov: NCT01105923.

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