Leveraging Real-time Data Analytics

This ACO implemented real-time clinical data analytics to improve respiratory patient care and reduce costs.

Every year, roughly 5,500 patients in the U.S. die from asthma attacks, despite the fact that respiratory illnesses are considered very manageable when cared for regularly (National Heart, Lung, and Blood Institute Data Fact Sheet. "Asthma Statistics."). Unmonitored respiratory conditions also lead to drastic increases in hospitalizations and medication utilization. With chronic and widely varying symptoms, patients with chronic obstructive pulmonary disease (COPD), asthma or allergies currently are among the most challenging and costly to treat. The problem, of course, is that most primary care providers have little time for chronic care management. 

Indeed, proactively managing respiratory conditions for a patient population is difficult for providers tasked with spending little more than six minutes per established patient. However, clinical data analytics can help reverse this trend by embedding respiratory care protocols within provider workflows. By extracting data from electronic health records (EHRs), analytics tools can help identify and track a patient's medical history, as well as help providers follow evidence-based standards for developing and delivering care plans. The challenge now lies in finding ways to use data analytics most effectively to close gaps in care. 

The Cumberland Center for Healthcare Innovation (CCHI), a 29-practice accountable care organization (ACO) spanning 14 counties and centered in Cookeville, Tenn., is at the forefront of using data analytics to improve care quality and reduce medical costs. Located in the "buckles" of both the nation's tobacco and seasonal allergy/asthma belts, CCHI has a patient base that includes a high proportion of COPD and allergy/asthma patients. In fact, when CCHI was first created, respiratory patients in the Upper Cumberlands were hospitalized two and a half times more often than the national ACO average. Currently, the ACO is striving to tame the challenges of managing patient care and workflow by leveraging real-time data analytics. 

A Different Mindset 

One of the primary problems in the fee-for-service medical world is that patient visits are geared toward providing care for the single condition they came in for - whether it is a bad back or skinned knee - and nothing more. Often overlooked are the chronic, moderately controlled conditions that could eventually result in additional medication, an emergency room visit, or worse. 

Concerns that typically prevent providers from effectively managing respiratory patients include: 
1. Doctors busy with presenting problems, urgent care matters and the management of daily practice operations often overlook non-emergent respiratory patient status. 
2. Many patients rarely visit their providers except for a yearly checkup or to treat the occasional cold. Physicians simply may not think to look back at patient histories to see if they happen to have asthma or COPD, or whether they have been appropriately queried about their respiratory status. 
Data analytics can help bring about a change in this acute care driven philosophy. It can proactively address the needs of the respiratory patient, ensuring they are caught up with all necessary chronic disease management steps - keeping them headed in the right direction and out of the emergency room. For providers, data analytics tools help drive critical clinical decisions. This improved type of deliverable data contributes to making people healthier, which correlates with reduced clinical costs. Furthermore, having data more easily accessible helps providers efficiently manage patient care. 

Changing Workflow & Data Management 

The process of improving the management of respiratory care is ongoing at CCHI, which uses analytical tools to help identify and manage clinical data. Currently, we at Cookeville Primary Care have a fully implemented process that will eventually be rolled out to the entire ACO. The process begins even before an office visit. A patient who calls the office can be identified through the records as asthma high-risk, for example. Some of the data points typically collected and analyzed include: 
• peak flow measurements; 
• office visits with diagnosis; 
• medicare claims; 
• emergency department visits; anad 
• hospital admissions. 

With the help of a disease registry, EHR and analytics tools, Cookeville Primary Care can track patients by disease process; in the near future, all CCHI practices will do the same. High-risk patients can be identified, but also under-the-radar patients, such as the moderate-persistent-asthmatic or moderately controlled COPD patients, who show up once a year for a checkup, are pinpointed. What the data analysis can do is help determine who might be at risk, and generate lists of patients who need specific medical issues addressed proactively. 

For example, patients identified as asthmatic are flagged, along with a notation about whether a peak flow or spirometry has been done within the past year. If not, nurses will perform that test when the patient checks in for an office visit - no matter whether the visit is for asthma or another condition. By obtaining this baseline data, providers can compare past data to present data to more thoroughly and effectively determine the status of respiratory conditions and plan of care. 

Tracking Chronic Disease 
In other words, data analytics will help CCHI providers keep track of chronic disease states even when office visits are for reasons unrelated to respiratory conditions. Whether a patient seeks treatment for a bad back or minor injury, analytics and EHR tools prompt questions about past respiratory issues. Decision support tools remind doctors or nurses to discuss a patient's recent respiratory medicine use; for example, if it has not been inquired about recently. If the patient is a tobacco user, the system can prompt for tobacco cessation efforts and advise a checkup every 6 months. 

Through the disease registry, tools can even coordinate all the patients who typically have asthma attacks at certain times of the year. This can be especially helpful in geographical areas where health conditions change seasonally. 

Proactive Workflows 

The 29 individual practices within CCHI all use vastly disparate EHRs and other technologies. Nevertheless, clinical analytics tools capable of bridging technology divides are empowering more proactive workflows that help providers adhere to evidence-based protocols. Respiratory disease is just one example of how the organization is using these tools. 

As an ACO, our goal is to develop best practices protocols within individual practices that gradually can be rolled out organization-wide. To help with this, some practices focus on asthma and COPD, while others focus on diabetes mellitus and other chronic conditions. Already this kind of mindset and effort is beginning to pay off, both in patient care and on the bottom line: An evaluation by one third-party payer of my practice revealed that ACO-type care as provided by our physicians cost $80/member/month less than the patients of other providers in town. 

CCHI does not achieve cost savings through HMO-style financial management, but through data-driven, proactive clinical service. The end goal is to keep patients with manageable chronic conditions out of the hospital. By closing clinical gaps, data analytics does more than just ensure a better ACO. It ensures happier and healthier patients. 

Harold (Hal) Chertok is a board certified family physician and primary care provider with Cookeville Primary Care Associates, and chair/president of the Board for Cumberland Center for Healthcare Innovation, a Medicare Shared Savings Accountable Care Organization.