Implementing a successful program to address gaps in care: An example of population-based approach in EMR systems

 

By Omid Shabestari, MD PhD, Director of Health Analytics, Carilion Clinic

 

Electronic Medical Record (EMR) systems are a must have for any healthcare organization. They allow providers to have timely and easy access to information of the patients they are interacting with. This access includes many different aspects of their care such as diagnoses, test results and interventions. These are very good examples of transaction-based information that is collected and reviewed on a daily basis. Several of the more advanced EMR systems offer opportunities to take best practice actions as the providers document information about different activities. These Best Practice Alerts (BPAs) help improve patient care.

Population health modules in EMR systems have been developed to address existing gaps in transactional-based systems.

There are certain use cases that the above model of human-computer interaction can fulfill very well. The initial required step in these use cases is the fact that a patient needs to interact with a provider prior to the computer being able to augment her or his care. The problem is that many patients are not aware of their care needs – either because they do not know about general preventive assessments for common conditions, or they have not received specific risk assessment for other conditions that they may be prone to.

The concept of population health has been used in different contexts. In healthcare finance, it is mostly used for grouping patients based on their payors and managing contractual obligations. In the clinical world, patients are grouped based on their medical conditions. The move toward risk-based contracts such as NextGen Accountable Care Organizations (ACOs) bring these two classifications close to each other with the opportunity for penalty reduction based on the identification of pre-existing conditions.

Population health modules in EMR systems have been developed to address existing gaps in transactional-based systems. Gaps in care can be divided into two categories, gaps in diagnoses and gaps in encounters. Gaps in diagnoses are about confirming and documenting pre-existing conditions to improve quality measures and reduce financial penalties for complications. Some of the examples of gaps in encounters are post-discharge visits by primary care physicians, screening for different types of primary cancer, follow-ups for early detection of cancer recurrence, scheduled assessments for chronic diseases such as Hb-A1c for diabetic patients, kidney function test for patients with Chronic Kidney Disease (CKD) and spirometry for patients with Chronic Obstructive Pulmonary Disease (COPD).

In addition to these built-in modules, there are third-party companies in health analytics that ingest EMR data from different organizations and calculate the risk for gaps in care. Having already achieved a good market share, these companies can overlay information from different healthcare systems and provide a more holistic picture of patient care. Identification of opportunities is a great starting point for closing care gaps, but the level of success with them depends on additional factors.

The requirement for success in these initiatives can be modeled by the three-legged stool analogy in which misalignment of any of the legs leading to instability. One of the legs, identification of gaps has already been discussed above. The other two legs can be represented by capacity and patient compliance. Even by using a risk-assessment predictive model with a high level of accuracy, considering the wide range of risk items, a large number of patients, and limitation of resources, addressing every risk is simply not feasible. Every healthcare system is dealing with limitation in the availability of clinical providers or other resources. These limitations affect the likelihood of comprehensive coverage of gaps in care. This is the stage where priorities need to be set based on the severity of risks and level of penalties attached to them. It becomes even more complex when one considers that different payor contracts may mandate different focus areas, and they should be stratified at the organizational level based on the proportion of the population under each contract. The allocation of resources needs to be carefully aligned by each healthcare system. Although this can be adjusted based on risks and contract changes, the outcome of these activities follow a late-effect pattern and should be given enough time to materialize. Regular monitoring of these measures allows operational leads to course correct in case initial assumptions are not materialized.

The third leg, patient compliance, is an item that has not received as much attention as the other two. Issues such as no-shows are a common problem in any healthcare systems. In addition, patients are not very willing to perform some preventive assessments, particularly invasive ones such as colonoscopy. Assessing the likelihood of compliance by patients is an important factor that helps in setting more realistic targets and expectations and get the best return-on-investment from these activities. This assessment can be used for more intensive follow up with people in need and dynamic resource allocation.

In summary:

closing gaps in care is a very complex process. Achieving this goal requires implementing multiple IT solutions to ensure a balanced activity that will result in the highest return on investment.