Today’s healthcare ethos places the patient at the center of decision-making and clinical care. But that hasn’t historically always been the case — under quantity-driven healthcare models of years past, processes sometimes superseded patient needs, which led to compromised outcomes.
Leading healthcare organizations are now depending on analytics to optimize processes A major goal of becoming more data-driven in this particular industry is improving patient experiences and results.
Here are five ways medical data analytics can improve patient outcomes.
Reducing Patient No-Shows & Increasing Adherence
Not only are patient no shows costly and inefficient for healthcare providers, but they also reduce the chances of patients getting the care they need in a timely manner. After all, only by showing up to appointments can patients receive important testing, prescriptions and advice.
Research has shown data analytics can help predict which patients are most likely to miss appointments using factors like “lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment.”
When healthcare providers can predict missed appointments ahead of time they can take extra care to minimize problematic predictors and reach out to patients more proactively with extra reminders ahead of upcoming appointments. This data-driven approach to reducing no shows can help patients receive care when it’s most beneficial — something that’s especially important for traditionally underserved segments of the population.
The bottom line? Better analytics can help medical providers ensure patients are adhering to treatments, drugs and therapies, which can in turn boost patient outcomes.
Predicting Risk of Disease
In a similar vein, healthcare providers can also lean on data analytics to predict those patients facing the highest risk of chronic disease based on personal and environmental factors. Early intervention, or a lack thereof, can make or break these patients’ outcomes. Data can sometimes identify risk before physical symptoms even manifest.
Getting New Treatments to Market
Drug research has traditionally been a slow process — which means patients can wait a long time for potentially life-altering treatments to be tested and approved for use. But data is helping to speed up this process.
Here’s an example: Scientists at one Fortune 500 pharmaceutical company use medical data analytics from ThoughtSpot to reduce the amount of time it takes to get insights on drug trials from three months to three minutes. Using a search-driven analytics tool, researchers can quickly analyze drug trial results and side effects by patient segment, which can help them develop treatments and get them approved faster.
Reducing Readmission Rates
Hospitals aiming to reduce instances of readmission following patient discharge can use data to assign risk levels to patient segments based on a wide variety of variables. The Children’s Hospital of Orange County is one organization that’s already done so, using four years’ worth of data chronicling 38,000 patients to better anticipate who is at risk for readmission.
Knowing a patient is high risk before discharging them can change how clinicians administer care plans and customize recommendations — with the aim of helping patients avoid readmission within the week or month.
Reducing Hospital Incidents
People go to hospitals to seek care, but unfortunately incidents can still occur that threaten the health and wellbeing of the patient. In fact, certain hospital-acquired conditions — like sepsis — can be deadly. Other examples include surgical errors, many types of infections, physical trauma and falls, kidney failure, blood incompatibility and more.
Hospitals can use the data they’ve collected about these incidents to figure out why they’re happening, then take targeted actions to reduce their occurrence.
There are myriad ways medical data analytics stand to keep improving patient outcomes, which is why competitive providers are making data a core part of their strategy.