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Data and analytics have been central to healthcare for decades. Now a major shift in how data is generated, aggregated, and used is brewing. The shift promises to turn data into the equivalent of a new wonder drug, powering the industry towards evidence-based care and new outcome-based models.
The nature of the shift is deceptively simple. Participants in the industry, from patients to providers, payors, pharmaceutical companies, and device manufacturers, will share their data and healthcare knowledge, triggering a massive surge of interest in analytics. When the data is integrated, the industry can expect to deliver a leap in the quality of care and a dip in the cost of care.
When compared to other industries such as manufacturing and banking, the healthcare industry has lagged in the use of Big Data and analytics. Forecasts suggest the industry is about to catch up. The global healthcare analytics market is expected to reach US$85.9 billion by 2027, up from US$7.3 billion in 2016. What’s powering this trajectory? For the most part, the five factors below are the ones that healthcare stakeholders need to be carefully tracking:
Value-Based Care: Accountable Care Organizations (ACOs) are emphasizing evidence-based treatment and outcome-based care, otherwise known as Value Based Care (VBC). This approach rewards physicians and healthcare organizations for outcomes rather than paying fees for services. Adherence to ACO standards provides financial incentives and penalties based on performance. In order to meet ACO standards, physicians and caregivers need to track datapoints like treatment details, recovery times, likelihood of remission/readmission, future lifestyle and treatment recommendations, and cost of care. ACOs, in turn, will use this data to both reward efficient care and prevent duplicative care. The key will be to safely and securely integrate data across physicians, clinicians, hospitals, laboratories, imaging systems, Electronic Health Records (EHRs), payors, and wearables. Analytics will provide a clear and dependable view of treatment and efficacy.
Whole Patient Records: The Whole Patient Record is a conceptual framework for sharing information across multiple providers, then merging that healthcare data with demographic and other personal data to influence care decisions. When a patient is diagnosed with diabetes by their primary care provider, optimal care would take into account a three-dimensional view of the patient. For example, what if they have also been diagnosed with depression, and also recently displaced from their home? Such factors can and should influence the care journey, by combining medical care with referral to a social worker who can meet their housing needs and help them access appropriate food options. While the concept of the Whole Patient Record is not new, it’s now much more feasible. The proliferation of digital data and the ability to share more readily across organizations can enable better care plans and ultimately better outcomes for patients.
Clinical Decision Support: Physicians have traditionally used their training, experience, and judgment to arrive at diagnoses and treatment decisions. Now they can supplement their expertise with data, which will allow them to serve more patients. Increasing doctor-to-patient ratios without compromising care will inevitably make care more cost-effective. However, data volumes are placing a mammoth cognitive load on physicians. Studies suggest that digitization in healthcare is currently responsible for a 48% per year growth in data, compared to the average of 40% across other industries. Fortunately, sophisticated analytics tools are stepping in with Clinical Decision Support (CDS) systems to ease that pressure. CDS tools organize and present data for clinical and diagnostic guidance, flag patient-specific disorders and allergies, and provide insights into optimal treatments.
Low-cost Technology: The availability of low-cost technology could not have happened at a better time for the healthcare industry. EHRs have traditionally been maintained by capital-intensive legacy systems. Today’s low-cost technology — Cloud, SaaS and IaaS, etc. — is putting inexpensive and scalable tools into the hands of care providers. More importantly, from a data and analytics point of view, the technology is also driving greater transparency, making it possible for payors and regulators to use EHRs for compliance. The growing access to reliable data has already encouraged around 70% of payors in the US to move to outcome-based plans. For patients, the upside of the data glut is just as striking. Payors are offering plans that are specific to their conditions, such as plans for therapeutic areas like infectious diseases, respiratory conditions, and cardiac issues. This change is a quiet revolution for patients who have had to pay vast amounts for broad medical insurance that doesn’t necessarily benefit them.
Data-driven Pharmaceuticals: Better treatment plans created in collaboration with pharmaceutical organizations are adding to the analytics-driven revolution in healthcare. With access to broader and deeper data, pharmaceutical organizations can engineer better formulations for diseases. But the more immediate outcome is of greater interest: With data and analytics, pharmaceutical organizations will be able to confidently provide assurance around their formulations. This will shift the trend from volume discounts to evidence-based outcome models.
In the near term, healthcare organizations are understandably concerned that the urgency to adopt analytics could result in uncertainty and chaos. Applications are now talking to one another across multiple organizations and over a variety of networks and platforms. Traditional methods of data exchange are being eclipsed, and new solutions inevitably bring a different set of security risks. These new data flows also introduce some difficult-to-answer-questions. For example: Does the care provider or the patient own the data? And who provides the authority to share the data?
