Pavan Kumar Chundi: Revamping the Business Ecosystem by Delivering Cutting-Edge Analytics Solutions and Data Insights
For modern-day businesses, gathering informative insights and deploying them to promote industry-focused practices has become quite critical. Almost all the big players in the industrial space are aiming to become agile and act quickly based on data-driven insights. Data has emerged as an invaluable asset for organizations. However, the challenge is to draw out actionable insights, which can be shifted to productive business goals and attain organizational objectives.
UC Health is an integrated academic health system serving the Greater Cincinnati and Northern Kentucky region. In partnership with the University of Cincinnati, UC Health combines clinical expertise and compassion with research and teaching – a combination that provides patients with options for even the most complex situations. Members of UC Health include: UC Medical Center, West Chester Hospital, Daniel Drake Center for Post-Acute Care, Bridgeway Pointe Assisted Living, University of Cincinnati Physicians and UC Health Ambulatory Services (with more than 900 board-certified clinicians and surgeons), Lindner Center of HOPE and several specialized institutes and centers, including: UC Gardner Neuroscience Institute and University of Cincinnati Cancer Center. Many UC Health locations have received national recognition for outstanding quality and patient satisfaction. Visit uchealth.com to learn more.
A Data-Centric Leader Advancing Digital Data Innovation
Pavan Kumar Chundi is the Business Intelligence and Data Analytics Manager at UC Health. As a key analytics leader at UC Health, he provides analytical solutions and assists executive leaders in setting strategic priorities by providing actionable insights. Currently, he supports multiple strategic initiatives involving patient safety, patient family experience, performance improvement, ratings and rankings, and is also the key member of the leadership team.
Pavan manages a team of 11 analysts and provides analytical support to all clinical divisions. He has been associating with the healthcare industry for over 15 years and has acquired a master’s degrees in Biomedical Engineering from the University of Surrey, UK, and Business Analytics from the University of Cincinnati. As a data analytics leader, Pavan has been instrumental in interpreting and analyzing data to develop analytics tools to improve outcomes. His areas of expertise include healthcare analytics, quality improvement, business intelligence, and data visualization.
Thanks to his expertise in healthcare analytics and quality improvement fields, Pavan was invited to be on the Advisory Board for the Master’s in Health Informatics at the University of Cincinnati. In this position, he mentors and provides technical guidance to graduate students. Over the years, Pavan has showcased his works at various Healthcare Conferences across the globe and has received various awards like the GLOBEE® 2022 Business Excellence Awards and Executive of the Year for Healthcare Products and Services. At the same time, he has authored and published articles in journals of international repute and serves on the Judge Panel for International Business Excellence Awards and as Chairperson for the 11th American Healthcare Summit.
Before joining UC Health, as a Lead Data Analytics Manager at Cincinnati Children’s Hospital Medical Center, he managed a team of six analysts and provided analytical solutions to network hospitals across the United States and Canada.
Deploying a Data Intelligence Approach to Advance Innovation
Here are some of the advanced innovations by Pavan that are driving transformation in the healthcare industry.
Bariatric Surgery Weight Loss Prediction Tool: Using predictive modeling techniques, Pavan designed a unique interactive weight loss prediction tool that not only serves as an informational tool for patients and families but also can be utilized by the clinical staff to monitor the progress and motivate patients to follow a treatment plan to achieve the desired outcomes. This interactive tool helps to predict how much weight a patient may lose every six months and their progression for the next three years, post-surgery.
Bariatric Surgery data and data for all subsequent visits post-surgery were extracted and formatted to get the required time stamps. Along with the inputs from the clinical team, literature was reviewed to obtain a comprehensive list of factors that could affect weight loss over time. Upon review, the data followed a monotone missing pattern (e.g., if a patient failed to show up at a certain visit, then the patient fails to show up at all the other subsequent visits). Before applying any kind of imputation technique, a review of the patient medical record was performed to track weight for the required time stamp. At each time stamp, a three-month time interval was used to check if the patient’s weight was recorded elsewhere in the hospital. To advance this procedure, five different regression models were created (e.g., one for each time stamp using the Piecewise Linear Regression technique). A 5-fold cross-validation was performed to measure the performance of the model.
Patient Reported Outcomes Analytical Framework: Pavan Kumar Chundi’s analytical framework on Patient Reported Outcomes (PROs) is a significant and outstanding contribution to the area of healthcare analytics. The framework helped to extract data from the questionnaires that patients answered at the time of their visits. The data was then transformed to take actionable insights that aided the clinicians to make informed decisions and improve patient outcomes. The importance of this framework is evident from the publications, in fact, the scientific community has also utilized this work.
The framework helped to implement the novel and sustainable application of several PRO’s in clinical practice. Specifically, it increased depression screening in children with Type1 Diabetes (T1D) to nearly 100%, identifying those at high risk for suicide, and providing resources to those children in acute need. This analytical framework created a standardized automated process that not only helped track the questionnaires but also helped the clinical providers to make informed decisions at the point of care. This enabled the team to develop patient-centered care and design early interventions to improve outcomes.
Outpatient Visit Length Prediction Tool: Besides, the above-mentioned innovations, Pavan also designed and developed a machine learning model that predicts the outpatient visit length. This innovative model incorporates data from variables on the day of the visit. When a patient checks in, the model automatically extracts data from variables of significance and provides an estimate of how long a visit might take including the wait time. The results from the initial pilots, conducted across 14 different outpatient clinics, were statistically significant and when his team surveyed patients and families about the accuracy of the model, 90% of them responded positively and said that the tool was of immense help.
Democratizing Data at its Best to Build a Healthy Community
By utilizing healthcare analytical products, healthcare organizations, ranging from single-physician offices and multi-provider groups to large hospital networks and accountable care organizations, stand to realize significant benefits. McKinsey estimates that healthcare data analytics can enable more than $300 billion in savings per year in U.S. healthcare and two-thirds of that through reductions of approximately 8% in national healthcare expenditures.
Clinical operations and Research & Development are two of the largest areas for potential savings, but organizations end up wasting $165 billion and $108 billion, respectively. With the increase of electronic record keeping, applications and other electronic means of data collection and storage, there is a significant amount of data being collected in real-time. These data sets are very complex and require people with extraordinary abilities to transform these data sets into actionable insights. Pavan’s innovations are great examples of where early interventions can help to improve the outcomes of patients and families.
Innovating Productive Solutions to Complex Healthcare Issues
Pavan’s analytical products have a positive impact on improving the clinical outcomes of patients. The bariatric surgery weight loss tool not only serves as an informational tool for patients and families but is utilized by the clinical staff to monitor the progress and motivate patients to follow a treatment plan to achieve the desired outcomes. Integrating this tool with primary care could help with earlier referrals and overall better outcomes.
Analytical framework on patient reported outcomes is a significant and outstanding contribution to the area of healthcare analytics. It also made a great impact on T1D Exchange, a non-profit organization focused on improving care and outcomes for individuals with type 1 diabetes through the T1DX-QI network.
The outpatient visit length prediction tool is an innovative approach that utilizes machine learning methodology to communicate with patients and families and thereby improving their experiences.
Empowering Healthcare Leaders to Provide World class Services
UC Health is a leader in all aspects of healthcare—clinical services, ground-breaking research and inspired teaching. With Greater Cincinnati’s largest and most distinguished group of board-certified physicians practicing in every medical and surgical specialty, UC Health is continually recognized for excellence and backed by the academic strength of the University of Cincinnati, one of the nation’s top 25 public research universities. UC Health is revolutionizing how discovery-driven care is delivered. Pavan’s innovative applications provide great support to UC Health’s clinical leaders and caregivers who provide patients with high-quality and innovative medical treatment and care.