Use Cases Demonstrate Information Governance Best Practices

Suzanne Paone, MBA, DHA, and Dilhari DeAlmeida, PhD, RHIA, both professors at the University of Pittsburgh, presented a well-attended session on information governance (IG) Tuesday afternoon. The session, “Information Governance, Best Practices and Lessons Learned,” demonstrated how information governance is leading the journey to innovation and experiential learning by leveraging technology investments and understanding information assets.

Data Governance vs. Information Governance

Paone explained the difference between IG and data governance. She said that IG boils down to who can make decisions about the data, while data governance is more concerned with the applicability and security of the data. She stated that many industries are ahead of healthcare in the management of data and that “if we understand our information assets, we can take better care of our patients.”

Background on Case Studies

The session centered on providing attendees recommendations regarding effective IG and development of data analytics. Paone and DeAlmeida presented two case studies that were developed from the combined efforts of the University of Pittsburgh and the University of Pittsburgh Medical Center (UPMC).

DeAlmeida presented on the two entities’ service model, which was developed and used to foster collaboration between the groups. Additionally, the Center for Assistance in Research using eRecord (CARe) was created for researchers as compliance orientation and a financial model, which included cost recovery estimates and a cost feasibility model to extract data and ensure the practice of IG.

The case studies were intended to reflect:

  • The importance of translational science and the application of knowledge beyond the research platform
  • The effective use and collaboration of multiple information systems in the context of electronic health records (EHR) systems
  • The process of effective data collection, de-identification, and storage of data
  • Ongoing prospective data collection (longitudinal data)
  • The dynamic use of online analysis and reporting utilizing decision support tools in conjunction with the EHR

The CARe Governance Model facilitated a unique research community that fostered clinician, IT, and administrator collaboration. It offered an opportunity to leverage analytics use case methods to address research projects and opportunities for researchers to contribute to enterprise analytics tool designs.

Use Case Lessons Learned

The first use case profiled was the “Blood Bank Study”followed by the “Diabetes Complications Study.”The blood bank study focused on the effective use and collaboration of multiple information systems. The diabetes study looked at population health, coding practices, and quality in the EHR, as well as defining and validating complications associated with diabetes via ICD-9 codes.

Internal and external data sources were used in the case studies with a focus on clinical impact. As a result of these studies, changes were made to many of the interfaces throughout the legacy systems. Cases were hand-picked because they involved known clinical and research problems that needed to be solved.

Descriptive, predictive, and prescriptive analytics assisted in meeting the goals of delivering clinical decision support at the point of care, measuring impact and clinical performance, measuring financial performance, and harnessing the value of electronic data. For example, different data sets can be analyzed to see how medications are being used and how statistical algorithms can predict those outcomes.

As a result of the research, day-to-day reporting will hopefully be replaced by the use of predictive analytics as lines are beginning to fade between research and clinical care.

Ongoing communication efforts include quarterly workshops where researchers post questions and walk through various protocols, as well as monthly governance meetings to address action items. In addition, the research groups invite former researchers to share experiences, challenges, and lessons learned.

Governance Challenges Identified

Some challenges identified with governance were defining data structures, dictionaries, definitions, alignment of multiple different systems, and helping stakeholders understand transactional data versus metadata.

Legacy systems and data structures were notably different because of proprietary data structures or data tools that not everyone has the skill sets to handle.

One of the biggest challenges identified was the overwhelming desire to analyze everything. Paone emphasized the need to determine what is important to the business side of clinical care.

“We are talking about a village, not a silo. We must have a fully integrated team,” Paone said.

All disciplines must be involved in information governance and use case strategies, including physicians, technology, finance, legal, operations, research, and HIM departments. Support is also needed to empower work teams to address data quality challenges with legacy platforms and apply project management processes to keep use case development on track and accountable to stakeholders.

Throughout the project, the lessons learned were consistent with AHIMA’s recent white paper on information governance in healthcare.

Catch up on the news and get insights from AHIMA’s 86th annual Convention and Exhibit held September 27-October 2 in San Diego, CA. For a complete list of event coverage on the Journal of AHIMA website, click here.

 

Submit a Comment

Your email address will not be published. Required fields are marked *