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A Markov model was developed for a study that examines the public health benefits and cost of a partially effective prophylactic HIV vaccine over 10 years in a population restricted to 15-year-old South African adolescent girls and future offspring. An important role is played by a partially effective HIV vaccine in HIV prevention in adolescents and infants in South Africa irrespective of other public policy implementations.

Abstract Background Developing a clinically relevant set of quality measures that can be effectively used by an electronic health record (EHR) is difficult. Whether it is achieving internal consensus on relevant priority quality measures, communicating to EHR vendors' whose programmers generally lack clinical contextual knowledge, or encouraging implementation of EHR that meaningfully impacts health outcomes, the path is challenging. However, greater transparency of population health, better accountability, and ultimately improved health outcomes is the goal and EHRs afford us a realistic chance of reaching it in a scalable way. Method In this article, we summarize our experience as a public health government agency with developing measures for a public health oriented EHR in New York City in partnership with a commercial EHR vendor. Results From our experience, there are six key lessons that we share in this article that we believe will dramatically increase the chance of success. First, define the scope and build consensus. Second, get support from executive leadership. Third, find an enthusiastic and competent software partner. Fourth, implement a transparent operational strategy. Fifth, create and test the EHR system with real life scenarios. Last, seek help when you need it. Conclusions Despite the challenges, we encourage public health agencies looking to build a similarly focused public health EHR to create one both for improved individual patient as well as the larger population health.

Background: Developing a clinically relevant set of quality measures that can be effectively used by an electronic health record (EHR) is difficult. Whether it is achieving internal consensus on relevant priority quality measures, communicating to EHR vendors' whose programmers generally lack clinical contextual knowledge, or encouraging implementation of EHR that meaningfully impacts health outcomes, the path is challenging. However, greater transparency of population health, better accountability, and ultimately improved health outcomes is the goal and EHRs afford us a realistic chance of reaching it in a scalable way.Method: In this article, we summarize our experience as a public health government agency with developing measures for a public health oriented EHR in New York City in partnership with a commercial EHR vendor.Results: From our experience, there are six key lessons that we share in this article that we believe will dramatically increase the chance of success. First, define the scope and build consensus. Second, get support from executive leadership. Third, find an enthusiastic and competent software partner. Fourth, implement a transparent operational strategy. Fifth, create and test the EHR system with real life scenarios. Last, seek help when you need it.Conclusions: Despite the challenges, we encourage public health agencies looking to build a similarly focused public health EHR to create one both for improved individual patient as well as the larger population health. [ABSTRACT FROM AUTHOR]

We describe the process of developing composite quality measures (CQMs). During the initial consultative process, we grouped quality measures based on the associated clinical workflow and difficulty, and then confirmed the groupings with factor analysis. The CQMs are estimated as the mean of the measures for each group. We used analysis of variance followed by a post hoc analysis to assess: (1) performance among the different CQMs each year; and (2) the performance trend for each of the composite measures from 2009 to 2011. The four CQMs were Control-BP, Control-Other, Assessment, and Screening. Performance was highest at baseline for Control-BP (58%, SD 15.07), followed by Control-Other (48.04%, SD 22.75), Screening (46.49%, SD 20.21), and Assessment (42.15%, SD 19.08). Performance on the CQMs increased significantly with time, whereas the gap between the CQMs decreased significantly over time. The CQMs reflect the clinical care domains, and practice performance is influenced by electronic health record functionality, clinician workflow, and clinical difficulty.