With increasing regulatory pressure, payers are keen to adopt new technologies, improve efficiencies, implement analytics and build interoperable platforms, but are left looking for ways to access clinical data from providers and other caregivers. The challenge is often in getting a single, standard view of patient data and translating this data into actionable intelligence.
Addressing Chronic Condition Management Challenges
According to the Agency for Healthcare Research & Quality (AHRQ), chronic conditions constitute nearly 70 percent of all healthcare expenses, and represent a significant cost driver for payers. Much of the expense occurs because, in the absence of clinical data, insurers often end up overlooking many highly preventable conditions that could potentially make a significant impact to future claims payouts.
Consider a scenario where a patient is diagnosed with a pre-diabetic blood sugar level and has a borderline high blood pressure of 130/85. While these look like mildly abnormal numbers, without timely treatment, the patients could be on the path to developing costly, debilitating, and life-shortening chronic diseases such as diabetes or heart failure.
tools can help to anticipate disease progression and then prevent it and future costs associated with disease.
Getting Better at Preventing Readmissions
Recently there has been a strong focus on aggregating data from each of the key stakeholders in the healthcare ecosystem – care providers, insurers, pharmacy benefit providers, laboratory systems, biometric devices, imaging applications, and even patients. The underlying focus across all departments, as they build capabilities to share and aggregate data, is ‘Cost Minimum, Care Maximum’.
For health plans as well as providers, one of the most important uses of aggregated data is to identify who is at the maximum risk of readmission. We are seeing many instances of predictive algorithms being used to help prevent unnecessary hospitalization. For example, Texas Health Harris Methodist Hospital of Hurst-Euless-Bedford uses predictive analytics software to find out which patients are at a higher risk of heart failure. This software has helped the hospital cut its 30-day readmission rate for heart failure conditions nearly in half.
Optimizing Resources for Utilization Management
Another area where analytics can be of great value is in predicting demand for healthcare services, such as the availability of emergency rooms or laboratory-related work. This ultimately would help in obtaining better staffing estimates for care providers, optimizing resource utilization and using the freed-up resources on preventive rather than curative care.
Utilization management models of the future would allow caregivers to have a unified and transparent view of the system’s performance, compare the resultant values with standard benchmarks and take corrective steps. This would help promote optimal care at the right cost through a collaborative payer–provider model and also help health plans’ clinical staff focus on the more complex exceptions that truly require their skills and expertise.
Increasing Payer Involvement in Care Processes
Many payers have begun to work closely with healthcare providers to build integrated healthcare analytics, through partnerships with large healthcare technology providers. In some cases, payers’ access to clinical data and analytics has allowed them to suggest optimal treatment options based on patients’ medical records. In other scenarios, case managers get analysis results based on case histories to ascertain whether or not the patients are diligently following doctors’ prescriptions.
For example: A payer could provide analytic support, using prescription and other data, to guide the physician toward patients who are most in need of attention so that their care can be prioritized. Ultimately, as the physician gets paid to identify disease early, the payer truly optimizes its efforts toward managing the medical risk in its portfolio, and patients get better-quality, more cost-efficient care.
Another example is a chronic condition like diabetes. The payer and provider might agree on the following three goals for diabetes care: the provider must identify all diabetes patients in his or her practice; all identified patients must receive a care plan; all diabetes patients must regularly receive the basic tests (hemoglobin A1c, a retinal exam, and kidney function test) to monitor their level of disease control.
This is clinical alignment, with the goal of early identification and treatment of a chronic disease. The benefits are reaped later, from saving the considerable costs incurred by an emergency such as diabetic coma or loss of vision.