Healthcare organizations are eager to address the rising problems of chronic diseases. They’re collecting a lot of data, adding analytics and even using genetic sequencing to spot potential health risks across patient populations.
But are these strategies moving the needle on population health management?
I address this question in a recent article with co-author Dr. Rasu Shrestha, vice president of medical information technology at University of Pittsburgh Medical Center (UPMC). The answer: Yes, but we have a ways to go.
3 steps to gain better insights from data
Chronic diseases – such as heart disease, stroke, cancer, diabetes and arthritis – are among the most common, costly and preventable of all health problems. Caregivers recognize that harnessing data is key to managing the health of patients, especially those with chronic disease.
Healthcare organizations need to focus on three areas to gain insights from this data:
1. Interoperability and openness – Creating the secure and free flow of information to deliver quality care, regardless of where patients are.
2. Actionable data – Generating accurate data, with discreet elements that providers can easily mine for trends, will help clinicians make better informed decisions at the point of care.
3. Patient engagement – Encouraging patients to take a proactive role and adopt healthier lifestyle habits, through more frequent data exchange with caregivers.
Many forward-looking organizations have advanced in these areas. Signs of early progress in population health management are visible in places such as University of Pittsburgh Medical Center (UPMC), one of the largest health systems in the United States.
Lessons we can learn from UPMC
UPMC is enormous. The health system includes 22 hospitals with 4,000 beds and 400 outpatient locations. For UPMC, connected healthcare means making it easier to find relevant data in a system that handles massive amounts of information, both from the payer and provider side.
The system’s 39,000 users and five million patient records generate 5.4 petabytes of data each year, and this volume is expected to double every 18 months. (For some perspective, one gigabyte is about 7 minutes of HD video, while one petabyte is about 14 years’ worth.)
UPMC’s strategy is to “filter the noise” and help clinicians easily access key nuggets of harmonized data with population health management tools, following these steps:
1. Start with interoperability. UPMC aggregates relevant data from different 48 different systems.
2. Organize the data to identify best practices by patient population. For example, UPMC identifies patients with congestive heart failure, and uses evidence-based guidelines to manage these populations more comprehensively.
3. Diagnose patients earlier for better outcomes. With better data, UPMC can provide preemptive diagnosis and earlier intervention.
4. Improve efficiencies. Better data is good for payers, too. UPMC can help identify gaps, research outcomes, and evaluate physician performance to gain operational efficiencies.
While UPMC has made strides toward managing population health, there are opportunities to build an even richer data set through genomics.
The hope and promise of genomics
Genetic and genomic information is becoming more accessible and attractive to clinicians and patients. UPMC is using data from newborns to facilitate early detection, diagnosis and intervention across a multitude of genetic, endocrine and metabolic disorders.
There are clearly still many questions for the industry to address. Does the patient own their data or does the provider? Would the patient have the choice to opt-out of providing their genetic information? As UPMC and other pioneers move forward with big data, analytics and genomics, we see early and hopeful signs of better healthcare outcomes.
What is your organization doing to improve chronic disease management? Leave your comment below.
Editor’s Note: You can read the full article, Big Data, Genomics and Managing Population Health, on H&HN Daily