3 reasons why population health data will be the new stethoscope
By Pranam Ben
The stethoscope is synonymous with physicians and healthcare. This instrument, created about 200 years ago in France, hasn't changed much since it was created. Meanwhile, incredible technological advancements in nearly every area of healthcare delivery have occurred.
One of these achievements that is particularly meaningful to us at The Garage is how far health information technology has progressed in the last decade, at a rate much faster than any other technological innovation in healthcare. With this rapid advancement, and associated high adoption rate among providers, data analytics technology for population health management (PHM) may soon be as synonymous with healthcare as the stethoscope. Here are three reasons why:
1.Deep, Holistic Analysis
Sophisticated data analytics platforms for population health management, such as The Garage's Bridge, are delivering deeper, actionable information much faster thanks to advancing computer-processing speed and algorithm complexity. Big data, as it is commonly known, is also becoming wide, too, as the definition of what constitutes a patient record is expanding beyond EHRs. Genetic, lifestyle and socioeconomic data sets are now accessible or integrated with platforms to assist providers with more accurate and reliable insight. Whether at the point-of-care or between encounters, this broad and holistic insight is crucial to helping at-risk patients navigate their care and manage the chronic conditions that increase costs and reduce care quality.
2. Machine learning and artificial intelligence
Larger amounts of data require more than just faster processing speed, they demand more intelligent programming to help providers make sense of the information-and most importantly-take action. Population health management platforms with machine-learning and artificial intelligence (AI) capabilities churn through vast amounts of data to identify trends, including those not even yet considered by the provider organization. This constant machine learning would be initiated by the healthcare organization simply inputting its established care quality and cost goals into the AI-powered platform, such as Bridge. Information is presented in an understandable and meaningful way to support efficiency, but also safe, prompt and effective clinical decisions.
3. Automated patient outreach
Taking action is crucial, but ACOs and other integrated organizations need to conserve clinical staffing resources to control costs. Understandably, care managers prioritize their limited time on highest-need patients. To alleviate the burden of some lower-risk population interventions, PHM technology is automating patient outreach, through text, email, phone and other means, to help patients re-engage with their care plan and overcome adherence obstacles. These initial contacts can be easily escalated to a live care manager to help providers prevent a near-risk patient from becoming high-risk and more quickly identify the outliers in populations who could potentially escalate care costs.
While a stethoscope is, and will probably always be, an indispensable instrument for clinicians, data analytics, AI and machine learning may soon prove equally as important to care delivery.
For more information on how data analytics, AI and machine learning are becoming synonymous with efficient and high-quality healthcare delivery, please read my recent article in Health IT Outcomes.
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