Design and assessment of a common, multi-national public health informatics infrastructure to enable H1N1 influenza surveillance.

Public health organizations in different nations face similar needs for gathering and analyzing population health data to detect and manage infectious disease outbreaks, including outbreaks of the 2009 Novel H1N1 Influenza A virus or "swine flu." This paper presents our progress to date on the design and assessment of a multi-national public health informatics infrastructure for data collection and disease surveillance. This initial work, under the aegis of an open health tools collaborative, lays the foundation for best practices in patient care and public health preparedness in the national health IT sector. This multinational collaboration is the first to identify essential electronic health record (EHR) data sets as well as standard public health informatics indicators to electronically monitor a notifiable public health condition internationally.

Real-Time Automated Hazard Detection Framework for Health Information Technology Systems.

An increase in the reliability of Health Information Technology (HIT) will facilitate institutional trust and credibility of the systems. In this paper, we present an end-to-end framework for improving the reliability and performance of HIT systems. Specifically, we describe the system model, present some of the methods that drive the model, and discuss an initial implementation of two of the proposed methods using data from the Veterans Affairs HIT and Corporate Data Warehouse systems. The contributions of this paper, thus, include (1) the design of a system model for monitoring and detecting hazards in HIT systems, (2) a data-driven approach for analysing the health care data warehouse, (3) analytical methods for characterising and analysing failures in HIT systems, and (4) a tool architecture for generating and reporting hazards in HIT systems. Our goal is to work towards an automated system that will help identify opportunities for improvements in HIT systems.

A proposed national research and development agenda for population health informatics: summary recommendations from a national expert workshop.

OBJECTIVE: The Johns Hopkins Center for Population Health IT hosted a 1-day symposium sponsored by the National Library of Medicine to help develop a national research and development (R&D) agenda for the emerging field of population health informatics (PopHI). MATERIAL AND METHODS: The symposium provided a venue for national experts to brainstorm, identify, discuss, and prioritize the top challenges and opportunities in the PopHI field, as well as R&D areas to address these. RESULTS: This manuscript summarizes the findings of the PopHI symposium. The symposium participants' recommendations have been categorized into 13 overarching themes, including policy alignment, data governance, sustainability and incentives, and standards/interoperability. DISCUSSION: The proposed consensus-based national agenda for PopHI consisted of 18 priority recommendations grouped into 4 broad goals: (1) Developing a standardized collaborative framework and infrastructure, (2) Advancing technical tools and methods, (3) Developing a scientific evidence and knowledge base, and (4) Developing an appropriate framework for policy, privacy, and sustainability. There was a substantial amount of agreement between all the participants on the challenges and opportunities for PopHI as well as on the actions that needed to be taken to address these. CONCLUSION: PopHI is a rapidly growing field that has emerged to address the population dimension of the Triple Aim. The proposed PopHI R&D agenda is comprehensive and timely, but should be considered only a starting-point, given that ongoing developments in health policy, population health management, and informatics are very dynamic, suggesting that the agenda will require constant monitoring and updating.

Improving adherence to guidelines for hypertension drug prescribing: cluster-randomized controlled trial of general versus patient-specific recommendations.

OBJECTIVE: To determine whether an intervention focusing clinician attention on drug choice for hypertension treatment improves concordance between drug regimens and guidelines. STUDY DESIGN: Cluster-randomized controlled trial comparing an individualized intervention with a general guideline implementation in geographically diverse primary care clinics of a university-affiliated Department of Veterans Affairs healthcare system. METHODS: Participants were 36 attending physicians and nurse practitioners (16 in the general group and 20 in the individualized group), with findings based on 4500 hypertensive patients. A general guideline implementation for all clinicians, including education about guideline-based drug recommendations and goals for adequacy of blood pressure control, was compared with addition of a printed individualized advisory sent to clinicians at each patient visit, indicating whether or not the patient's antihypertensive drug regimen was guideline concordant. We measured change from baseline to end point in the proportion of clinicians' patients whose drug therapy was guideline concordant. RESULTS: The individualized intervention resulted in an improvement in guideline concordance more than twice that observed for the general intervention (10.9% vs 3.8%, t = 2.796, P = .008). Bootstrap analysis showed that being in the individualized group increased the odds of concordance 1.5-fold (P = .025). The proportion of patients with adequate blood pressure control increased within each study group; however, the difference between groups was not significant. CONCLUSION: An individualized advisory regarding drug therapy for hypertension given to the clinician at each patient visit was more effective in changing clinician prescribing behavior than implementation of a general guideline.

Translating research into practice: organizational issues in implementing automated decision support for hypertension in three medical centers.

Information technology can support the implementation of clinical research findings in practice settings. Technology can address the quality gap in health care by providing automated decision support to clinicians that integrates guideline knowledge with electronic patient data to present real-time, patient-specific recommendations. However, technical success in implementing decision support systems may not translate directly into system use by clinicians. Successful technology integration into clinical work settings requires explicit attention to the organizational context. We describe the application of a "sociotechnical" approach to integration of ATHENA DSS, a decision support system for the treatment of hypertension, into geographically dispersed primary care clinics. We applied an iterative technical design in response to organizational input and obtained ongoing endorsements of the project by the organization's administrative and clinical leadership. Conscious attention to organizational context at the time of development, deployment, and maintenance of the system was associated with extensive clinician use of the system.

An intelligent case-adjustment algorithm for the automated design of population-based quality auditing protocols.

We develop a method and algorithm for deciding the optimal approach to creating quality-auditing protocols for guideline-based clinical performance measures. An important element of the audit protocol design problem is deciding which guide-line elements to audit. Specifically, the problem is how and when to aggregate individual patient case-specific guideline elements into population-based quality measures. The key statistical issue involved is the trade-off between increased reliability with more general population-based quality measures versus increased validity from individually case-adjusted but more restricted measures done at a greater audit cost. Our intelligent algorithm for auditing protocol design is based on hierarchically modeling incrementally case-adjusted quality constraints. We select quality constraints to measure using an optimization criterion based on statistical generalizability coefficients. We present results of the approach from a deployed decision support system for a hypertension guideline.

A framework for evidence-adaptive quality assessment that unifies guideline-based and performance-indicator approaches.

Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a unified model representation and algorithm for evidence-adaptive quality assessment scoring that can: (1) use both complex case-specific guidelines and single-step population-wide performance-indicators as quality measures; (2) score adherence consistently with quantitative population-based medical utilities of the quality measures where available; and (3) give worst-case and best-case scores for variations based on (a) uncertain knowledge of the best practice, (b) guideline customization to an individual patient or particular population, (c) physician practice style variation, or (d) imperfect reliability of the quality measure. Our solution uses fuzzy measure-theoretic scoring to handle the uncertain knowledge about best-practices and the ambiguity from practice variation. We show results of applying our method to retrospective data from a guideline project to improve the quality of hypertension care.

Developing quality indicators and auditing protocols from formal guideline models: knowledge representation and transformations.

Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a model representation and algorithm for deriving structured quality indicators and auditing protocols from formalized specifications of guidelines used in decision support systems. We apply the model and algorithm to the assessment of physician concordance with a guideline knowledge model for hypertension used in a decision-support system. The properties of our solution include the ability to derive automatically context-specific and case-mix-adjusted quality indicators that can model global or local levels of detail about the guideline parameterized by defining the reliability of each indicator or element of the guideline.