Healthcare Analytics: Analytics and machine learning
Two essentials for success in value-based medicine
By Steve Curd
The U.S. healthcare system is well on its way in the transition to value-based payment models that reward providers for delivering quality outcomes and keeping patients healthy. In fact, as of March 2016, the Department of Health and Human Services reported that an estimated 30 percent of Medicare payments were already tied to these new alternative payment systems.
Value-based programs are replacing traditional fee-for-service models that pay providers based on the number of services delivered. The newer models are designed to encourage care that is well-coordinated, cost-effective and lead to quality patient outcomes. In order to achieve new payment objectives, providers are seeking opportunities to engage patients in their own care, improve patient satisfaction and keep patients healthier.
Keeping patients healthy is critical not only for individual providers, but the U.S. economy as a whole. In 2012, about half of all adults suffered from at least one chronic disease such as diabetes, cancer, congestive heart failure (CHF) or chronic obstructive pulmonary disease (COPD). One in four adults had two or more chronic conditions. Treatment of individuals with chronic disease is expensive and accounts for 86 percent of the nation’s total healthcare costs.
Under value-based care models, providers must proactively manage the health of individuals with chronic illness to curtail costly complications that can lead to hospitalization, hospital readmission and/or early death. Many chronic diseases are linked to unhealthy behaviors, such as lack of physical activity, tobacco use and poor nutrition. Providers are motivated to closely monitor these behaviors and take action to keep patients healthy.
The proliferation of smart, wearable health devices and remote monitoring systems afford providers more opportunities to assess their patients’ health outside of traditional care settings. If unhealthy behaviors are identified, providers can attempt to engage patients in the care process and help them make healthier choices. While changing patient behavior is not an easy task, new technologies are helping providers motivate their patients to be more engaged in their own care. Other emerging technologies are leveraging sophisticated algorithms and machine-learning technology to help providers identify and predict adverse events, which in turn facilitates customized care processes, earlier interventions and fewer complications.
Traditional Care Models
Traditional fee-for-service (FFS) payment systems reimburse providers based on the delivery of services. For example, when a CHF patient has an office visit, the physician is reimbursed for treating the patient. If for some reason the patient has complications and has to seek treatment in the ER, the hospital and the treating physician are reimbursed for that episode of care.
Providers are not paid to monitor the patient’s health between visits and have no financial incentive to make sure the individual is living a healthy lifestyle and following the recommended treatment plan. In a FFS world, providers have little need for advanced analytics to predict outcomes or customize treatments, or for remote monitoring technologies to track patients’ health and behaviors.
New Models Drive Need for New Technologies
The healthcare industry often lags behind other sectors when it comes to technology adoption. However, the shift to value-based medicine is fueling the need for innovations that help providers achieve payment objectives.
Remote health monitoring systems, for example, have become increasingly sophisticated in recent years. These systems give providers the ability to monitor vitals, symptoms and other health data between office visits. A variety of mobile apps allow users to record their health information manually or via Bluetooth-enabled devices, and then transmit details to their providers. If the collected data suggests a decline in health, providers can take corrective action early, before complications ensue. Ultimately these technologies help reduce the care costs associated with unhealthy lifestyles and chronic conditions, and enhance the quality of life for individuals.
In the past, providers have rarely been paid for remote care services or for monitoring patient health between visits. Today, however, Medicare and most Medicaid programs cover some telehealth services, as well as certain non-face-to-face services under Medicare’s Chronic Care Management Services CPT billing code. As these technologies continue to mature, look for expanded coverage for remote patient monitoring services.
Another innovation gaining wider acceptance is technology that enhances patient compliance of prescribed medical regimens. For example, by using personality assessment tools that are customized for healthcare, providers can evaluate an individual’s risk of non-compliance and then tailor treatment plans and communication styles based on each patient’s personality. Certain patients, for instance, may respond better to caregivers who communicate in a direct and matter-of-fact style, while others are more motivated by caregivers who adopt a more personal and sympathetic approach. When a patient’s unique personality is taken into account, communication is enhanced, and the patient is more motivated to remain engaged in the prescribed care plan. Outcomes are ultimately enhanced, as is patient satisfaction.
Machine Learning and Predictive Analytics
Machine-learning technologies and predictive analytics have been utilized for decades across a number of industries. In recent years, the healthcare sector has begun adopting these technologies for a variety of applications, including chronic disease management, staffing predictions and population health risk assessment.
Analytics provide valuable insights into the health of an individual based on collected data and contextual information. Over time, as additional data is captured and available for analysis, these insights become more precise. In the world of value-based medicine, this type of data is critical for predicting the likelihood of adverse events so that caregivers have adequate time to enact proactive measures that enhance outcomes.
Furthermore, the utilization of machine learning allows providers to gain insight into the effectiveness of existing programs and protocols and identify the treatments and interaction styles that yield the best results for specific patients with specific conditions. This customized approach to care is at the cornerstone of the Precision Medicine Initiative, which seeks to tailor medical decisions, practices, and/or products to the individual patient based on each person’s genetic makeup, environment and lifestyle.
A Value-Based Medicine Example
Consider the care process for an individual with CHF when provider payments are dependent on the delivery of quality outcomes and care that is cost-effective and well-coordinated. Regardless of whether the person was first seen in the physician’s office or the hospital, the provider’s primary goal would be to stabilize the patient’s health and minimize the risk of high-cost care in the hospital.
Traditionally a CHF patient may have office-based visits with physician exams two to 12 times a year and lab tests and echocardiograms as needed. Between visits, the individual is advised to manually track his weight on a daily basis, and keep a record of symptoms, physical activity and diet. If any significant changes are noted, the patient is told to contact his physician.
If the patient alternatively has access to remote monitoring devices, much of the tracking and communication can be automated. Sensor technologies and wireless communications can capture patient health data in real time. Then, cloud-based analytics can be deployed to evaluate current and historical vitals and symptom data alongside contextual data. Caregivers can extract insights to predict adverse events and assign risk scores that reflect the individual’s condition. Providers can tailor future care based on the patient’s specific results, and/or take proactive measures as necessary. Because the information is processed in real time, providers are able to take action earlier and prevent emergency situations.
As healthcare continues its shift to value-based medicine, providers will need new technologies to manage the health of their patient population, improve outcomes, and keep costs under control. This is especially true when caring for individuals with chronic conditions.
Analytics and machine-learning technologies are two innovations that will be essential for providers seeking to improve the quality of care. By incorporating these technologies into traditional care processes, providers will be better equipped to address the unique needs of individual patients and proactively manage their care.
Steve Curd is CEO of Wanda, a company dedicated to advancing the effectiveness and efficiency of medicine by using machine learning in place of conventional technologies and by enabling clinicians to make more informed care decisions.