A complete guide to giving you an overview of Data science application in HealthCare
As healthcare organizations develop more sophisticated big data analytics capabilities, they are beginning to move from basic descriptive analytics towards the realm of predictive insights.
Predictive analytics may only be the second of three steps along the journey to analytics maturity, but it actually represents a huge leap forward for many organizations.
Instead of simply presenting information about past events to a user, predictive analytics estimates the likelihood of a future outcome based on patterns in the historical data.
This allows clinicians, financial experts, and administrative staff to receive alerts about potential events before they happen, and therefore make more informed choices about how to proceed with a decision.
The importance of being one step ahead of events is most clearly seen in the realms of intensive care, surgery, or emergency care, where a patient’s life might depend on quick reaction time and a finely-tuned sense of when something is going wrong.
Let’s discuss a few of the real-time use cases where data science can be applied in the Health care sector.
RISK SCORING FOR CHRONIC DISEASES: Prediction and prevention go hand-in-hand. Organizations that can identify individuals with elevated risks of developing chronic conditions as early in the disease’s progression as possible have the best chance of helping patients avoid long-term health problems that are costly and difficult to treat.
Creating risk scores: Based on lab testing, biometric data, claims data, patient-generated health data, and the social determinants of health can give healthcare providers insight into which individuals might benefit from enhanced services or wellness activities.
“Across all [reimbursement] models, the identification, stratification, and management of high-risk patients is central to improving quality and cost outcomes,” says the Association of American Medical Colleges (AAMC)
“The use of predictive modeling to proactively identify patients who are at highest risk of poor health outcomes and will benefit most from intervention is one solution believed to improve risk management for providers transitioning to value-based payment.”
DIAGNOSIS: The National Academies of Sciences, Engineering, and Medicine estimates that around 12 million Americans receive misdiagnoses, which can sometimes have life-threatening repercussions. A BBC article notes that diagnostic errors cause an estimated 40,000 to 80,000 deaths annually.
One of the most effective uses of data science in healthcare is medical imaging. Computers can learn to interpret MRIs, X-rays, mammography, and other types of images, identify patterns in the data and detect tumors, artery stenosis, organ anomalies, and more.
Stanford University researchers have also developed data-driven models to diagnose irregular heart rhythms from ECGs more quickly than a cardiologist and distinguish between images showing benign skin marks and malignant lesions.
POST CARE MONITORING: After any type of surgery or treatment, there is the risk of complications and recurring pain, which can be difficult to manage once the patient leaves the hospital. Remote in-home monitoring helps doctors stay in touch with patients in real-time while freeing limited and costly hospital resources.
Intel’s Cloudera software helps hospitals predict the chances that a patient will be readmitted in the next 30 days, based on EMR data and socioeconomic status of the hospital’s location.
HOSPITAL OPERATIONS: Hospitals are cost-sensitive and face complex operational problems, such as how many staff to assign at certain hours to maximize efficiency, how to ensure enough hospital beds are available to meet patient demand, and how to enhance utilization in the operating room. Predictive analytics can optimize scheduling and even go so far as to tell hospital staff which beds should be cleaned first and which patients may face challenges during the discharge process.
Furthermore, business intelligence can streamline billing, identify patients who are at risk of late payments or financial difficulties, and coordinate with financial, collections, and insurance departments. The Centre for Medicare and Medicaid Services saved $210.7 million by applying big data analytics in fraud prevention.
PREVENTING SUICIDE AND PATIENT SELF-HARM: Early identification of individuals likely to cause harm to themselves can ensure that these patients receive the mental healthcare they need to avoid serious events, including suicide.
In a 2018 study conducted by KP and the Mental Health Research Network, the combination of EHR data and a standard depression questionnaire accurately identified individuals who had elevated risk of a suicide attempt.
Using a predictive algorithm, the team found that suicide attempts and successes were 200 times more likely among the top 1 percent of patients flagged.
The strongest predictors of a self-harm attempt included mental health or substance abuse diagnoses, previous suicide attempts, the use of psychiatric medications, and high scores on the depression questionnaire.
Conclusion: We can conclude that the application of Data Science in Healthcare is very vast, there is a lot of scope for this in the current market as its one of the fastest-growing domain currently.
