Healthcare Analytics: ACO Practices Improve Reporting Process with Clinical Data Analytics Tool
By Larry Allen, MD
Collecting and analyzing clinical data to measure performance is no simple task for many physician practices, but is becoming increasingly critical as government and commercial payers shift to value-based payment contracts and programs (Block, 2013).
Our group, Indiana Lakes Accountable Care Organization (IL ACO) has not been a stranger to these clinical performance-monitoring challenges. Comprised of 25 primary care practices (15 are part of Indiana University Health Goshen Physicians [IUHGP] and 10 are independent), IL ACO became a participant in the Medicare Shared Savings Program (MSSP) in January of 2013. One of the program’s requirements for earning the shared-savings financial bonus is that the ACO must submit an annual report attesting its performance on 33 quality measures, including 22 metrics driven by primary care management of care coordination, patient safety, preventive health, diabetes, hypertension, ischemic vascular disease, heart failure, and coronary artery disease.
Adding to the data analysis complexity is the ACO’s structure: practices are independently managed and use EHR systems from four different vendors. This lack of clinical data and systems integration would have required the group to spend hours compiling data on Excel spreadsheets, which did not allow for the timely reporting the ACO needed to identify and address gaps in care.
In late 2013, however, our group implemented a clinical data analytics software application that integrated with all of the affiliated practices’ EHR systems and collected data for any health-related metric in real time. With this tool, providers can view a dashboard for each patient, or for populations of patients, and quickly ascertain performance on MSSP quality indicators based on the most recent clinical data. The application delivers timely information from within IUHGP and it also integrates data from the other affiliated ACO practices and hospitals, so the ACO has a comprehensive view of the process and outcomes of the ACO measures.
Although recently implemented, the clinical data analytics tool has already saved practices and administrators from numerous hours of compiling and analyzing data, which is a significant cost savings. On the clinical side, because the application delivers more timely and relevant reporting to providers, care gaps are identified sooner and interventions can be completed faster. Our group is confident that this continued data capture and analysis will lead to further improved quality metrics, reduced costs, and will enhance shared savings revenue from the MSSP.
Selecting the Optimal Technology
IL ACO, due to its affiliation to the statewide Indiana University Health system, had access to numerous clinical data analytics applications. We investigated three such applications, but none of the systems were able to automatically extract the relevant MSSP data from the practices’ EHRs for reporting.
Undeterred, we began to research applications that were being used by other ACOs for clinical data quality analysis. In mid-2013, we attended an MSSP webinar featuring a presentation by an ACO from Tennessee (Ross, 2013) and how they were using a clinical data quality analysis application across their 26 rural independent primary care practices with 14 different EHR systems. Noting the similarities between our structure and the other ACOs, IL ACO began performing due diligence on the application. We signed a contract for the technology in fall 2013 and began implementation in November.
Rapid Implementation
Despite a lack of information systems integration, the application implementation was rapid, requiring only some minor customization due to the variations in data capture within the practices’ EHRs and differences in physicians’ clinical documentation styles, such as word choice preferences. Once installed, though, the application learned and adapted to variations in physicians’ terminologies and normalized the data so subsequent analysis would be accurate and reports would be understandable throughout the group.
A major advantage was that an on-site technician was not required for implementation; rather, the application was uploaded to IUHGP’s EHR servers through the Internet with the vendor’s technician supporting the deployment live through web-conferencing software. We first installed the application on the practices’ most common EHR system platform and then on the second most utilized until all four EHR platforms were linked through the technology. In total, the software required approximately 20 minutes to upload per practice before it was automatically capturing data for quality analysis. After less than six weeks, the tool had already aggregated and analyzed enough meaningful MSSP data to share with physicians to support their decision-making processes.
In addition, although the application is deployed on our servers, its analytics engine is cloud-based, which allows the vendor to automatically deliver software updates and optimizations, so our practices can concentrate on creating reports and improving performance.
Adoption and Workflow Integration
While relevant MSSP data from all practices affiliated with the Indiana Lakes ACO are captured by the application, physicians—even those within IUHGP-owned practices—were not required to monitor performance with the analytics tool or generate reports other than those that were required by the Medicare program.
To encourage adoption, each practice of the ACO is able to view the up-to-date performance outcomes of all the ACO primary care practices via the cloud-based dashboard. Physicians were motivated by this ACO-wide disclosure of their practices’ performance and became more engaged with the application. Even the busiest physicians are able to access the tool’s graphical dashboard feature, which allows them to quickly gauge performance on any measures they choose.
Since implementation, practices have incorporated the application into workflows in different ways. For daily usage, some practices assign a health educator or registered nurse health coach to use the application’s dashboard for identifying patients who would most benefit from interventions. Other practices use the application for pre-visit chart reviews to analyze patients’ gaps in care, such as missing tests or lab results, and then complete those orders during their scheduled visit. Additionally, educators certified by the American Diabetes Association are using the dashboard to identify diabetic patients who would most benefit from diabetes education, and ACO health coaches use it to help identify and educate patients about other conditions.
Regardless of the workflow, the IL ACO practices do not segregate the Medicare and non-Medicare patients. Rather, the application is used to improve clinical quality performance for all commercially insured or uninsured patients, as well. This strategy not only helps improve outcomes for all patients, but it also prepares the medical group for potential value-based payment programs of commercial insurers.
Encouraging Positive Impact
Although the application was deployed less than one year ago, many of the IL ACO practices have already experienced clinical performance improvements, namely in smoking cessation counseling and interventions. Shortly after software implementation, we noticed one practice with a significant increase in the number of patients who reported quitting smoking as well as adhering to smoking cessation medication prescriptions. Other practices followed this high-performing physician’s protocol and dedicated more time to cessation counseling and medication. Overall, rates for smoking cessation have improved among patients across the ACO.
Another encouraging result of implementing the application is that ACO care coordinators use it to educate high-risk/high-cost patients in the case management program about their gaps in care. Identifying these patients was previously a challenge because it required time-consuming research in the various EHRs to piece together the patient data that showed the total view of the patients’ gaps in care. Additionally, the application is being used for targeted outreach, such as sending notifications to diabetic patients who are past-due for lab tests.
Tackling the largest cost driver, chronic conditions, continues to be the focus for the both the ACO and IUHGP and will demand more time before significantly better metrics are detected. We are confident, however, that with our new powerful clinical data analytics capabilities and simple, rapid access to reports, that management of these conditions will improve, which will reduce costs and improve the likelihood that our ACO will earn the shared savings incentive revenue from Medicare.
Larry Allen is chief medical officer of Indiana University Health Goshen and the CEO of Indiana Lakes ACO, a partnership of IUHGP and private physicians. He may be contacted at lallen15@IUHealth.org.
REFERENCES
Block, J. (2013, March 26). Value-based insurance plans gain momentum. Modern Healthcare. Retrieved July 16, 2014, at http://www.modernhealthcare.com/article/20130326/NEWS/303269958
Ross, F. (2013). ACO Quality Reporting: Using Information Technology to Achieve Success. Centers for Medicare and Medicaid Services webinar. Retrieved July 16, 2014, at
http://www.youtube.com/watch?v=8jKVfw9DKrw