This blog is part of a two-part series featuring guest authors who are on the front lines of healthcare. Today’s post features Greg McKelvey, MD, MPH, Head of Clinical Insights at KenSci. Dr. McKelvey sat down with us to explain how KenSci is reducing clinical variance and care utilization with data science. Learn more about how KenSci Clinical Analytics is revolutionizing the health care industry on Microsoft AppSource.
As the cost of healthcare continues to rise, care providers are constantly seeking opportunities to lower costs while improving the quality of care. Reducing clinical variance and care utilization are critical to maximizing the efficiency and efficacy of the healthcare system – ultimately bringing down costs and making care more affordable and accessible for everyone. Minimizing clinical variance and care utilization requires leveraging advanced data science and analytics tools, which empower doctors and care teams to transform daily operations in hospitals and keep patients healthier for longer.
Let’s take Jane, a 40-year-old, overweight, and diabetic individual with high blood pressure. Despite her numerous care providers, many doctor visits, and thousands of spent dollars, Jane is still struggling to manage her health. She has been to the hospital twice this year for diabetes-related conditions. Imagine if in just one visit, Jane and her provider could easily create a cheaper preventive treatment plan that’s helped countless other people like Jane avoid hospital stays, yet is customized specifically for her.
Data science is about to change the way care providers interact with patients like Jane. Analytics are transforming the entire care continuum, saving lives, optimizing budgets, and improving the efficacy and efficiency of care at every step. This is possible because data analytics can instantly identify patients’ personal risk factors, provide accurate predictions of their healthcare needs, and suggest optimal interventions.
With data analytics, doctors provide proactive care in a way that has proven effective for patients like Jane – saving both time and money. Let’s look at how data-based preventive medical care and targeted acute care empower Jane’s doctors to provide preventive plans, help prevent complications during a hospitalization, and provide her with an individualized aftercare plan upon discharge from the hospital.
Minimize visits to care providers with tailored, preventative plans
With smart devices and Jane’s online health logs, her providers receive continuous, fully-integrated monitoring of Jane’s health between appointments, enabling them to adjust her treatment regimen regularly – as opposed to the once or twice a year when she comes in for a checkup. Jane’s doctors leverage data science to optimize her treatments and promote lifestyle changes they know have helped others with the same conditions, using models trained on the health histories of all kinds of diabetic patients. Machine learning technology makes it easy for patients and care teams to track metrics such as diet, exercise, vital signs, laboratory studies, and medications. Utilizing those granular insights, care providers are empowered to prescribe preventative activities and check in with their patients outside the clinic or hospital setting – enhancing targeted treatment decision-making and heading off any potential medical emergencies looming on the horizon.
Receive better care during a hospital stay
Data science not only improves preventive care – it transforms inpatient care as well. Let’s imagine that diabetes self-care, diet, and exercise modification do not have the intended effect and Jane has a diabetic complication leading to a hospital admission. Rather than manually checking her vitals every four hours and comparing them against population-based, aggregated “normal” values, data analytics software monitors her vitals and instantly compares them with her own historical trends. The analytics surface that Jane routinely has higher blood sugar levels compared to patients with a similar diagnosis and demographic. When her blood sugar levels descend into a range that is normal for most people, they are actually dangerously low for her, and the medical staff is notified. With an early notification that Jane’s “normal” blood sugar levels are trending substantially lower, her care providers quickly adjust her medication regimen to avoid a low blood sugar level. Proactive adjustment enables Jane to recover faster and avoid more serious complications, aiding her to leave the hospital sooner.
Benefit from a personalized discharge and aftercare plan
Once Jane’s condition is stable, hospital staff begin contemplating her discharge plan. Releasing Jane too soon puts her at risk of being readmitted. Postponing her discharge, however, also comes at a cost. Jane’s risk of a hospital-acquired infection will increase, and she’ll be using resources that another patient could need more urgently. Before data analytics, discharge decisions were made based on whether patients like Jane met subjective criteria or an aggregate population-based standard of medical stability. Data-driven healthcare provides hospital staff with the information necessary to make appropriate discharge decisions that optimize both Jane’s health and the hospital’s efficiency. Data also empowers the staff to send Jane home with a customized post-care plan that takes her complete health status into account, so her recovery is as quick and painless as possible.
Transform care today
KenSci Clinical Analytics helps care teams improve overall health for individuals like Jane. The providers and facilities that treat her also benefit by allocating their limited resources more efficiently. Clinical analytics utilizes the breadth of healthcare data to provide actionable insights that prevent readmissions, improve patients’ and providers’ experiences, and tailor post-care plans. With targeted insights before, during, and after her most recent hospital stay, Jane is on the path to a better quality of life.
Learn more about how KenSci Clinical Analytics is revolutionizing the health care industry on Microsoft AppSource.