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Background: Evaluating the quality of care provided by individual primary care physicians (PCPs) may be limited by failing to know which patients the PCP feels personally responsible for.Objective: To develop and validate a model for linking patients to specific PCPs.Design: Retrospective convenience sample.Participants: Eighteen PCPs from 10 practice sites within an academic adult primary care network.Measurements: Each PCP reviewed the records for all outpatients seen over the preceding 3 years (16,435 patients reviewed) and designated each patient as "My Patient" or "Not My Patient." Using this reference standard, we developed an algorithm with logistic regression modeling to predict "My Patient" using development and validation subsets drawn from the same patient set. Quality of care was then assessed by "My Patient" or "Not My Patient" designation by analyzing cancer screening test rates.Results: Overall, PCPs designated 11,226 patients (68.3%, range per provider 15% to 93%) to be "My Patient." The model accurately categorized patients in development and validation subsets (combined sensitivity 80.4%, specificity 93.7%, and positive predictive value 96.5%). To achieve positive predictive values of > 90% for individual PCPs, the model excluded 19.6% of PCP "My Patients" (range 5.5% to 75.3%). Cancer screening rates were higher among model-predicted "My Patients."Conclusions: Nearly one-third of patients seen were considered "Not My Patient" by the PCP, although this proportion varied widely. We developed and validated a simple model to link specific patients and PCPs. Such efforts may help effectively target interventions to improve primary care quality. [ABSTRACT FROM AUTHOR]

Hospitals are under great pressure to reduce readmissions of patients. Being able to reliably predict patients at increased risk for rehospitalization would allow for tailored interventions to be offered to them. This requires the creation of a functional predictive model specifically designed to support real-time clinical operations. A predictive model for readmissions within 30 days of discharge was developed using retrospective data from 45,924 MGH admissions between 2/1/2012 and 1/31/2013 only including factors that would be available by the day after admission. It was then validated prospectively in a real-time implementation for 3,074 MGH admissions between 10/1/2013 and 10/31/2013. The model developed retrospectively had an AUC of 0.705 with good calibration. The real-time implementation had an AUC of 0.671 although the model was overestimating readmission risk. A moderately discriminative real-time 30-day readmission predictive model can be developed and implemented in a large academic hospital.

Background: Data to support improved patient outcomes from clinical decision-support systems (CDSSs) are lacking in HIV care., Objective: To test the efficacy of a CDSS in improving HIV outcomes in an outpatient clinic., Design: Randomized, controlled trial. (ClinicalTrials.gov registration number: NCT00678600), Setting: Massachusetts General Hospital HIV Clinic., Participants: HIV care providers and their patients., Intervention: Computer alerts were generated for virologic failure (HIV RNA level >400 copies/mL after a previous HIV RNA level ≤400 copies/mL), evidence of suboptimal follow-up, and 11 abnormal laboratory test results. Providers received interactive computer alerts, facilitating appointment rescheduling and repeated laboratory testing, for half of their patients and static alerts for the other half., Measurements: The primary end point was change in CD4 cell count. Other end points included time to clinical event, 6-month suboptimal follow-up, and severe laboratory toxicity., Results: Thirty-three HIV care providers followed 1011 patients with HIV. In the intervention group, the mean increase in CD4 cell count was greater (0.0053 vs. 0.0032 × 109 cells/L per month; difference, 0.0021 × 109 cells/L per month [95% CI, 0.0001 to 0.004]; P = 0.040) and the rate of 6-month suboptimal follow-up was lower (20.6 vs. 30.1 events per 100 patient-years; P = 0.022) than those in the control group. Median time to next scheduled appointment was shorter in the intervention group than in the control group after a suboptimal follow-up alert (1.71 vs. 3.48 months; P < 0.001) and after a toxicity alert (2.79 vs. >6 months; P = 0.072). More than 90% of providers supported adopting the CDSS as part of standard care., Limitation: This was a 1-year informatics study conducted at a single hospital subspecialty clinic., Conclusion: A CDSS using interactive provider alerts improved CD4 cell counts and clinic follow-up for patients with HIV. Wider implementation of such systems can provide important clinical benefits., Primary Funding Source: National Institute of Allergy and Infectious Diseases.

Background: Evaluating the quality of care provided by individual primary care physicians (PCPs) may be limited by failing to know which patients the PCP feels personally responsible for., Objective: To develop and validate a model for linking patients to specific PCPs., Design: Retrospective convenience sample., Participants: Eighteen PCPs from 10 practice sites within an academic adult primary care network., Measurements: Each PCP reviewed the records for all outpatients seen over the preceding 3 years (16,435 patients reviewed) and designated each patient as "My Patient" or "Not My Patient." Using this reference standard, we developed an algorithm with logistic regression modeling to predict "My Patient" using development and validation subsets drawn from the same patient set. Quality of care was then assessed by "My Patient" or "Not My Patient" designation by analyzing cancer screening test rates., Results: Overall, PCPs designated 11,226 patients (68.3%, range per provider 15% to 93%) to be "My Patient." The model accurately categorized patients in development and validation subsets (combined sensitivity 80.4%, specificity 93.7%, and positive predictive value 96.5%). To achieve positive predictive values of > 90% for individual PCPs, the model excluded 19.6% of PCP "My Patients" (range 5.5% to 75.3%). Cancer screening rates were higher among model-predicted "My Patients.", Conclusions: Nearly one-third of patients seen were considered "Not My Patient" by the PCP, although this proportion varied widely. We developed and validated a simple model to link specific patients and PCPs. Such efforts may help effectively target interventions to improve primary care quality.