CDC Health Information Innovation Consortium (CHIIC) May 17 Forum
Tuesday, May 17, 2016 from 10:00 – 11:00am ET
Chamblee Building 106, Room 1A + webinar
- Introduction – Brian Lee – 10 minutes
- Exploring Practice-Level Analytic Solutions for Smaller Health Care Practices that Support Population-Level Analytics – Hilary Wall in the National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP) and Jim Jellison from the Public Health Informatics Institute (PHII) – 20 minutes
With Meaningful Use and the supporting CMS EHR Incentive Programs, clinicians who implement a certified electronic health record (EHR) theoretically should now have access to a robust pool of patient data. Despite this influx of EHR data, some clinicians have difficulty accessing it due to resource or personnel limitations. This is also true for public health professionals who have a need for population-level clinical data but may not be able to access the data or receive it in a way that is most meaningful for their purposes.
States need technical guidance on how to best engage with and support clinical settings with moderate- to low-health information technology capacity. There are software solutions that exist to help fill the gap for practices without robust analytic capacity. However, no comprehensive guidance exists that summarizes the features and gaps for these types of products; this is desperately needed as clinicians receive escalating pressure to deliver high-quality care. On the flip side, to improve surveillance and target interventions for key health issues, public health professionals need access to aggregated clinical data stratified by pertinent public health demographics (e.g. age, sex, race/ethnicity, zip code, and insurance status) that can be pieced together from multiple clinical settings to provide a community profile.
The project sought to:
- inventory population health management software solutions for clinical settings without access to a clinical data warehouse and
- evaluate the solutions against to-be-established criteria that reflect needs of both clinicians and public health, and
- provide recommendations for future projects that are needed to address gaps in the data flow from patient visit to clinical quality improvement to public health surveillance. These deliverables will be useful tools for clinicians and direct technical assistance providers to clinicians (e.g. state and local departments of health, Regional Extension Centers, Quality Improvement Networks).
To view the full project description, click here.
3. Cloud Based Scalable Clinical Decision support (CDS): A Case Study – Ninad Mishra, MD in the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention – 20 minutes
Health IT incentives are probably not enough for all hospitals, especially community hospitals, and small practices to develop and support their own systems for clinical decision support as knowledge management of the logic is both time consuming and costly. Having these rules inserted into different EMR systems has health IT capital investment requirements and would need dedicated IT staff at every site of implementation. CDS offered as a service, that is ideally based on emerging national standards such as Health-e Decisions (HeD) and Clinical Quality Framework (CQF), has potential to solve some of these problems.
The Division of STD Prevention (DSTDP), in a multi-year effort, has partnered with the National Association of Community Health Centers (NACHC), Alliance of Chicago, GE Healthcare, Health Departments, and Qvera to develop and deploy an automated CDS service to deliver clinical screening guidelines directly to an EMR system. In Phase I of this project, a number of health centers prototyped the use of clinical decision support developed within individual EMRs to alert for Chlamydia screening in eligible population. This work was particularly important in identifying considerations in the appropriate collection of data and presentation of decision support within the clinical workflow. While underlying standards were uniform across all of the applications, the work involved considerable duplication, and there was no inherent mechanism to uniformly and efficiently update the tools as standards and recommendations change and evolve. The use of CDS as a service in phase 2 was the natural next step in the evolution of this idea. In this phase, DSTDP working with the informatics office in the Center established a prototype service for the CDS to offer a scalable solution to deploy clinical decision support as a service that remains up-to-date in terms of the clinical content and recommendations. Based on success and lessons of the previous phases, CDC and NACHC, in phase 3 of the project, are now partnering with Ohio Shared Information Services (OSIS) group of health centers, NextGen and possibly Epic to test this prototype in a diverse technical and clinical environment.
This presentation will discuss the technical infrastructure of the clinical service as well as showcase the integration of computerized logic in EMR systems resulting in a CDS intervention.
4. Discussion & Suggestions – 10 minutes
- Exploring Practice-Level Analytic Solutions for Population Health ManagementCdc-pdf
- Population Health Management SolutionsCdc-pdf
- Cloud Based Scalable Clinical Decision support A Case StudyCdc-pdf
- Items of interestCdc-pdf
- Forum notesCdc-pdf
Presentation 2 – Cloud Based Scalable Clinical Decision support (CDS): A Case Study – Ninad Mishra
Presentation 2 Q&A
[Marion Tseng – City of Chicago] Could you elaborate on the 3 phases of the project?
[Ninad] Phase 1 with CHCs- hard wired knowledge management, phase 2 web service based knowledge management with Alliance (GE centricity), phase 3 with OSIS and OCHIN (NextGen and Epic respectively)
[Marion Tseng – City of Chicago] How did you select the health centers to participate in the 3 phases respectively?
[Ninad] We did not directly select any of them, we have cooperative agreement with National Association of CHCs and they help us find willing partners.
[Marion Tseng – City of Chicago] What are your next steps and expected outcomes for phase 3?
[Ninad] To see how much of processes deployed to work with Centricity can be replicated with Epic and NextGen. What would be the main challenges in standardization of such distributed knowledge management service.
[Marion Tseng – City of Chicago] Do you follow up with the health centers/network to evaluate the impact of the clinical decision tools?
[Ninad] Yes, but we wanted to make sure that the technology works first, and we don’t directly go to process and outcome evaluation as research shows only a proper implementation of local CDS based on 5R would likely have an impact on the outcome. These are more intractable problems and we want to parse and tackle them on by one.
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