A key aspect of practicing medicine is anticipating and mitigating risks based on knowledge gleaned from past and current patient data. That’s precisely the intersection at which predictive analytics is changing the health care industry. Health care providers are constantly asking themselves questions: If we perform this surgery, what’s the likelihood of complications? If we discharge this sick patient now, what are the chances we’ll see them again in the ICU? Does this machine need to be serviced now, or can it wait?
There are few areas in life that offer absolute certainties, and medicine has certainly never been one. Even with predictive health analytics in health care, there still isn’t absolute certainty. But with the development and more frequent use of predictive analytics, it’s become a bit easier for physicians to make more informed and definitive choices.
What Does Predictive Analytics Do?
In short, the goal of predictive analytics in health care is to show medical professionals the probability of events occurring and the likely outcomes of the events. By using predictive analytics in this way doctors can sometimes prevent problems before they even have to cure them. Algorithms that can impact the health of a single patient as well as a small group or an entire population can now be used thanks to the rise of machine learning, artificial intelligence (AI) and the Internet of Things (IoT).
In a recent survey, 85 percent of health care organizations viewed data analytics priorities as fundamental to achieving broader strategic goals.1 They’re seeing improved satisfaction from patients, reduced costs and better care outcomes. So how can health care organizations truly harness the power of predictive analytics and turn data into innovative practices that support better patient care? Here are four predictive analytics in health care examples that show the possibilities of this technology.
Reducing Patient Readmission
Hospital readmissions aren’t fun for anyone, especially the patients. But it also impacts the entire organization, doctors and payers—hospital readmissions cost Medicare $26 billion each year.2 On top of that, hospitals are fined heavily for readmissions under Medicare’s Hospital Readmission Reduction Program. 82% of hospitals in the 2019 reduction program were fined.3
A healthcare facility network called UnityPoint Health wanted to figure out why so many of their patients were being readmitted, so they decided to ask the patients themselves. Upon readmission patients were asked, “Why do you think you are back?”. Patients’ answers ranged from not having the funds to buy necessary medication to being unable to schedule follow-up appointments. UnityPoint Health collected the answers and fed them into a predictive model that proceeded to assign readmission risk scores to each patient visiting the hospital. This enabled physicians to receive predictions about when patients would experience symptoms, allowing them to allocate time and resources in advance so that when the patient called again, they had the ability to treat them and avoid hospital readmission. Within 18 months of using the predictive analytics tool, UnityPoint had reduced readmission by 40%.4
Managing Population Health
Managing population health sounds like a very broad goal, and as such, it covers three different aspects of predictive analytics in health care:3
Identifying chronic diseases: By scoring patients based on demographics, age, disabilities and patterns of previous care, predictive analytics can identify those who are at risk for chronic conditions and give them care before the disease progresses.
Spotting patterns in public health: Predictive analytics in health care can identify possible population health trends. In a study published by The Lancet Public Health Journal, in which they used predictive analytics to spot trends and discovered that alcohol-related liver diseases will rise and cause deaths unless drinking patterns in the U.S. change.
Identifying infection disease outbreaks: Could predictive analytics in health care have warned us about COVID-19? Yes. In fact, a Canadian predictive analytics and AI solutions company called BlueDot did just that. In December of 2019 BlueDot put out a warning about a spike in unusual pneumonia cases in Wuhan. A little over a week later, the World Health Organization made an official statement declaring the emergence of a novel coronavirus. Even now predictive analytics help public health officials and ordinary people keep tabs on current and future trends and mutations of the virus.
Predicting Disease Progression and Comorbidities
More and more health care institutions are using predictive analytics to identify patients whose conditions might become more serious. For example, by using this technology health clinicians can more accurately predict which diabetes patients are likely to develop renal disease. They can also spot patients who are becoming septic and save their lives before their condition reaches that point. Health care professionals can greatly improve outcomes for patients by identifying disease progression early and implementing early interventions.
Diagnoses Through Wearable Tech
Wearables, or electronic devices with multiple sensors to monitor activities and conditions of our bodies, have grown in popularity in the last decade. The evolution from the Nike Fuel Band which counted steps and distance to the latest Apple Watch which can track steps and distance, as well as provide ECG and Pulse Oximeter readings is astounding. Individuals can measure their own vitals on the spot and even send them to their personal physicians, making wearables excellent data collection tools in healthcare.5
However, there’s so much data collected that the ever-expanding databases will need to be analyzed and organized by AI algorithms in order to identify relationships between metrics and diseases. With predictive analytics helping to make those connections and predictions, doctors will be able to diagnose illnesses earlier and recommend optimal treatment choices.
Establish Your Future in Health Care
If you’re ready to harness the power of predictive analytics and use the power of information to help improve patient outcomes and health care functions, consider how Marquette’s online Master of Science in Health Care Data Analytics will prepare you for the dynamic career you’re looking for. In as little as two years you can be on your way to a powerful career in health care. Speak with an Admissions Advisor to find out more about the online M.S. in Health Care Data Analytics, or start your application today.
- Retrieved on March 31, 2022, from businesswire.com/news/home/20211014005289/en/New-Study-Reveals-20-of-Healthcare-Organization-Executives-Fully-Trust-Their-Data
- Retrieved on March 31, 2022, from healthcaredive.com/news/ma-patients-readmission-rates-higher-than-traditional-medicare-study-find/557694/
- Retrieved on March 31, 2022, from itrexgroup.com/blog/predictive-analytics-in-healthcare-top-use-cases/#
- Retrieved on March 31, 2022, from managedhealthcareexecutive.com/view/how-three-hospitals-use-predictive-analytics-reduce-readmissions
- Retrieved on March 31, 2022, from intel.com/content/dam/www/public/us/en/documents/solution-briefs/benefits-health-analytics-wearables-brief.pdf