An Introduction to Accountable Care Organizations (ACO’s) and Health Data Exchange and the importance of a foundational data management platform for healthcare and life sciences
Coordinating data across all points of contact – clinical, claims and pharmacy – is critical to providing coordinated healthcare. Outcome-based reimbursement, population health management and personalized medicine all rely on accurate and actionable data intelligence. That’s where big data comes into play. Healthcare organizations need to have a Big Data solution in place that provides actionable intelligence to those who need it, when they need it. During this blog series, I will outline some of the key aspects of the new health ecosystem in the United States and demonstrate how foundational data lake management platforms, such as Zaloni’s, can assist with progressing these health and life science agendas through actionable, timely and accurate data.
Let’s start with an overview of accountable care, a current and rapidly accelerating health care movement mandated by US law. The Affordable Care Act (ACA) strives to achieve three goals, called “The IHI Triple Aim”. These goals are improving the experience of care for individuals, improving the health of populations, and lowering per capita costs.
Better Care For Individuals
The first focus area under Triple Aim is improving the quality of care that a patient receives. Physicians and other caregivers have been working with the government to define what quality means and how to measure it. The five key quality domains the federal government has identified and will be using to incent improvement are:
- Patient experience of care
- Care coordination
- Patient safety
- Preventive health
- At-risk population/frail elderly health
Better Health for Populations
Most health care is delivered on a one-on-one basis, so measuring and changing the health of populations is a grand undertaking. Populations can be organized by geographic location, age, gender, race, medical condition, and the list goes on. Better health for populations is about improving the health of various groups through efforts such as increased vaccination rates, prevention and wellness programs, and lowering obesity rates.
Lower Total Costs of Care
Through many of the recent health reform initiatives, providers, insurers, and policy makers are zeroing in on reducing the total costs of care for an individual over time. Many argue that the current level of U.S. healthcare spending, which is about 17 percent of gross domestic product (GDP), is not sustainable and must be reduced, particularly if we want to provide health insurance to all.
An Accountable Care Organization (ACO) can best be viewed as the logical or preferred organizational end state that is achieved through health data exchange in order to address the three goals of “The IHI Triple Aim”.
Health data exchange across multiple servicing caregiver boundaries can lead to dramatic improvement in the quality and continuity of care information that is shared in a timely manner. It enables care coordination across multiple providers and organizations and is critical to achieve optimal levels of clinical integration and population health management. This exchange of data can only be realized through the proper technological framework. Properly managed big data lakes are proving to be the most effective architectures for ACO collaboration. Let’s use an example scenario to demonstrate.
Mr. Christensen is a 56 year old auto worker with a history of obesity, hypertension and diabetes, he has early retinopathy but with no other evidence of other organ changes from his chronic diabetes. He was recently laid off from his job at a small auto parts supplier and is looking for employment when he falls ill in December. He reluctantly goes to his primary care physician (PCP) Dr. Jameson with fever, acute shortness of breath, and wheezing in the lower left lobe of his lungs. Dr. Jameson diagnoses him with acute pneumonia (confirmed on a chest x-ray) and admits him to the Local Community Hospital (LCH).
In the hospital Mr. Christensen is diagnosed with renal disease (demonstrated through elevated serum creatinine, proteinuria and mild acidosis). At discharge he is placed in a home care program for strict monitoring of sodium and protein intake along with diabetes monitoring. He is discharged to the local home care agency to be seen by a visiting RN, Ms. Tracy Miller. His discharge medications include insulin, an oral antibiotic and two new medications – a brand name diuretic and a new ACE inhibitor for renal disease and hypertension.
During the RNs third visit to Mr. Christensen she becomes concerned with his rising blood pressure, weight gain and general lethargy. She calls Dr. Jameson to order new lab tests, which she then draws blood and delivers the sample to the lab herself. The nurse questions Mr. Christensen who insists he is compliant with his medication program.
The local community hospital, the home care agency, Dr. Jameson’s practice, the local laboratory and the local community pharmacy which fills Mr. Christensen’s prescriptions all belong to a common area-based ACO, and so the clinical findings on Mr. Christensen are published in a common electronic community health record powered by the Michigan Lakes Health Care – Health Information Exchange (MLHC HIE) and its Managed Data Lake technology framework. The community health record features an innovative patient management dashboard, accessible electronically to all of integrated members of Mr. Christensen’s extended healthcare team.
Tracy, the nurse notices that the list of medications from the LCH discharge summary does not reconcile with the list from local pharmacy and during further discussions with Mr. Christensen learns that he filled the two inexpensive generic prescriptions but not the expensive new brand name diuretic and ACE inhibitor. With the recent economic downturn, his impending layoff and lack of ongoing health insurance coverage from his employer, Mr. Christensen admits he cannot afford to take the two additional brand name medications for renal disease.
Tracy also receives Mr. Christensen’s recent laboratory test results via the dashboard. It indicates deterioration of renal function, serum creatinine levels and electrolytes suggesting a recurrence of metabolic acidosis. Dr. Jameson then switches Mr. Christensen to an alternative generic medication for his renal disease and sends an electronic referral to a new nephrologist Dr Brad Toruk to see Mr. Christensen immediately for a more intensive evaluation of his worsening renal disease.
Because the visiting RN and Dr. Jameson are part of an ACO with links to the MLHC HIE technology that supports high quality care and efficient practices, they were able to intervene quickly with the use of real-time and accurate patient health data to prevent another expensive readmission to the hospital. Without the collaborative capabilities provided by the technology framework and the underlying managed data lake infrastructure, such a successful outcome would be in doubt, and certainly less efficient and more expensive.
This sample scenario illustrates how proper ACO care coordination leveraging a managed data lake and collaborative HIE capabilities results in prevention of emergency hospital admissions and adherence to proper standards of care.