You can’t treat a patient without data. From the minute a patient enters a clinic, hospital, or doctor’s office, data is being collected. Demographics, symptoms, medical history, family history, and many other data points are requested and recorded to begin the process of diagnosis and treatment. Medical professionals listen to, and examine, patients in order to gather data about their condition. Lab tests, x-rays, and the like are ordered to obtain additional information. All of this data is processed and interpreted throughout the patient encounter.
Consider the diagnostic test of an MRI. This study is appropriate in specific circumstances. In order to make the test useful, several things must happen:
- Data must be collected from the patient for the physician to review.
- The physician must use the data to identify the need for that specific test.
- The test is performed.
- The test is interpreted, generally utilizing the skills of a specialist, in this case a radiologist.
- The treating physician must then apply the test results to the unique needs of the patient. This requires an understanding of the power of the data as well as its limitations. The physician must know which data points are the most important and will have the biggest impact on the patient. He or she must also consider what findings the test may have missed, and what findings are of little consequence.
- The patient is counselled regarding the test results, and involved as an active decision-maker in the treatment process.
- Diagnoses and treatments are formulated using data obtained throughout the clinical encounter, including the MRI.
- The response to treatment is monitored and the cycle of data collection begins again, as the physician continues to care for the patient.
- The same principles of data collection, interpretation, and intervention apply on a much larger scale. It is easy to see the correlation between this process and the “scientific method” that drives discovery across various disciplines.
Like patients, health systems need checkups, preventive care, and treatments for various ailments. Central to being successful in these endeavors is the effective utilization of data. While every healthcare provider is comfortable using data like an MRI to guide direct patient care, unique challenges arise in the diagnosis and treatment of a clinical practice, hospital system, managed care organization, or patient population.
These challenges must not dissuade healthcare providers and leaders from seeking out the data that is vital to healthier systems. As with any patient encounter, the data may not be perfect, and will require a skilled eye to interpret and identify opportunities. Nevertheless, such data analysis is crucial to the delivery of quality healthcare.
Proskriptive has recently partnered with Saint Francis Health System in Missouri to help identify obstacles and opportunities. The same pattern of data-driven diagnosis and treatment that is utilized in patient care is being leveraged to meaningfully enhance the operations of this health system. Through this partnership, Proskriptive and Saint Francis will work together to improve care delivery, enhance efficiencies, support providers, close gaps-in-care, and improve the patient experience. Here is how the pattern is being applied:
- System-specific challenges, needs, and opportunities are considered. This relies on insights identified through internal data, as well as reference to benchmarks and external standards.
- Potential data sources (internal) are identified that will provide insight into these system-specific challenges, needs, and opportunities.
- Data (internal) is collected accordingly in a focused and meaningful manner.
- Data is interpreted, utilizing the skills of clinical and data science experts.
- The data is applied to the unique needs of the health system. Benchmarks (external data) are further incorporated and opportunities are identified. This requires an understanding of the power and the limitations within the data. The most impactful data points are thus identified.
- Appropriate stakeholders within the health system counsel with data science experts regarding the findings, and are active decision makers in the intervention plan.
- Interventions are implemented using data obtained throughout the evaluation.
- The impact of interventions is monitored and the cycle continues, as the health system advances.
This high-level framework provides the roadmap for a cycle of improvement that can be scaled to the needs of any healthcare system. It provides a pattern that facilitates mindful assessment and intervention. This in no way minimizes the importance of clinical expertise, leadership experience, or an innate understanding of a system’s unique features. Those and other qualities remain integral to understanding the data, and being able to act upon it in a meaningful way.
Great power lies within data, and through leveraging this power, health systems are transformed. This leads to more effective care delivery, enhanced provider support, greater patient satisfaction, and healthier communities. Continual progress toward these goals depends on asking the right questions, understanding the answers, and moving forward in a data-driven manner.
About the Author: Dr. Ryan Heyborne is committed to finding solutions to the challenges that abound in the healthcare environment. He currently serves as the Chief Medical Officer for Proskriptive, supporting health data analytic solutions that improve care delivery and patient outcomes. Dr. Heyborne is an emergency physician and remains clinically active. He obtained his MD from the University of Utah and completed Emergency Medicine Residency training at Indiana University. He obtained his MBA in 2015 from Northwest Nazarene University. Dr. Heyborne worked with Blue Cross of Idaho for over four years, serving as the Senior Medical Director. He also currently provides support for the Idaho Medicaid Program as the State Medical Director for Telligen.