Put Home Monitoring Efforts Where They Will Do The Most Good

By Martin S. Kohn, MD, MS, FACEP, FACPE, Chief Medical Scientist, Sentrian

Fully one-third of the money spent on healthcare in the United States – roughly $900 billion dollars a year – is wasted on things of no value.[i] That recognition is driving the most significant trend among healthcare providers today: A concerted effort to simultaneously improve quality and control cost. That dual goal marks a distinct change from past efforts, which focused only on controlling utilization and not improving clinical outcomes.

To get to the goal, providers must first focus on the population of patients that accounts for approximately 70 percent of the healthcare spend in the US – those with established chronic disease. Chronic disease patients, such as those with congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) or diabetes, experience frequent hospitalizations and other expensive acute care interventions. Thus, targeting established chronic disease is an important effort in transforming healthcare, improving outcomes and curbing expenditures. Although the ideal would be a health system that can prevent patients from developing chronic diseases, the burden is chronic diseases dominates the current scene.

Many of the hospital admissions for chronic disease patients occur after long intervals during which they had no contact with their clinicians. The idea of monitoring such patients at home, to close that communication gap, has been around for at least 20 years. By using devices to record measurements such as weight, blood sugar, and blood pressure, the hope was that patient deterioration could be identified early, allowing an intervention that would reduce the need for hospitalization. Some of the programs were able to reduce avoidable hospitalizations for small groups of patients in pilot programs, but were so labor intensive that they could not be scaled to large numbers of patients and were very expensive.[ii] [iii] [iv]They often involved frequent phone calls or home visits to follow-up on alerts, many of which were false positives. A sustainable program has to be both clinically and economically effective.

Early versions of home monitoring systems – first generation, if you will – made numerous simplifying assumptions. They focused on patients with one disease, such as CHF, and ignored the patients’ comorbidities, even though older patients typically have multiple chronic diseases, such as CHF and COPD or diabetes. The early systems further assumed that the state of the one disease could be represented by a single measurement, such as patient weight for CHF, where it’s assumed that an increase in weight reflects fluid retention and a worsening of the condition. Additionally, a one-day change in the measure, such as a two- or three-pound weight gain from one day to the next, was the threshold for an alert that the patient was deteriorating. Those assumptions served to simplify the monitoring effort but resulted in many false positives (there are many reasons why a measured weight could vary from one day to the next) and missed opportunities to detect deterioration early enough to intervene to reverse the process and keep the patient out of the hospital. The rate of false positives currently hovers around 70 percent, which means a substantial fraction of staff time is spent chasing down false positives.

A program that treats all patients in the same way but benefits only a few patients may provide clinical value, but its sustainability would be threatened by high cost. An important component of effective prevention programs such as home monitoring is identifying the patient for whom the program has value. Careful choices about target populations and the nature of the program will make the prevention strategy more effective clinically and economically.[v]

An effective approach has several components:

• Determine the range of diagnoses in a target group of patients to maximize the potential benefit.
• Assess the patients for impactibility. Since monitoring patients who won’t benefit from monitoring adds costs with no value, choosing patients more likely to be helped by monitoring is important. For example, a patient with a chronic diagnosis such as COPD but without any acute care encounters related to that disease is not a prime candidate since there are no acute events to prevent.
• Track multiple parameters, chosen for the patient’s diagnoses. A patient with just COPD might be monitored for pulse, blood pressure, oxygen saturation and peak flow rate, while a patient with COPD and CHF could also have a weight scale.
• Limit the patient’s burden. Since patients may be reluctant to participate if they are expected to work with too many devices, limit the number of devices requiring active participation on the part of the patient, while still providing sufficient data. Monitor device technology is changing rapidly, with new devices making multiple measurements passively, not requiring the patient to actively perform the measurements, which may require less effort on the part of the patient.
• Track longer term trends as well as day to day changes in order to detect subtle progression that would otherwise be missed.

Collecting data is relatively easy, but unless it is transformed into actionable insights it provides little value. Sentrian provides the analytics engine to detect patterns in the collected data that predict which patients are likely to deteriorate - days before they become acutely ill. That advance warning allows the clinicians to initiate a modest intervention to reduce the likelihood of serious deterioration.

Personalized healthcare, making decisions that are more likely to be beneficial for an individual, is an important part of improved decision making.[vi] The same principal applies to home monitoring. Not all CHF patients are the same. A patient with CHF and COPD is likely to have different predictive patterns than a patient with CHF alone. Improving the monitoring process requires learning from experience. Machine learning, part of the Sentrian approach, allows our predictive power to improve over time. The outcome of prediction, whether it was accurate or false, is fed back into the system, gets processed and results in suggestions for improvement. Those results are reviewed by domain experts to help decide if the change is valuable. The analytic system can generate its own suggestions for improvement (unsupervised machine learning) or incorporate expert input (supervised or expert-enhanced machine learning). In this way, the longer it works with a group of patients, the better it gets at predicting deterioration. It is a step toward personalized healthcare in that the system can define a cohort of very similar patients (e.g. male, over 75 with COPD, CHF and Diabetes) that would benefit from a specific predictive model.

The result is an entire toolbox filled with tools that are best-suited to the job of keeping each individual healthier at home. And helps nail down the right treatment for each.

About The Author
Martin S. Kohn, MD, MS, FACEP, FACPE is Chief Medical Scientist of Sentrian, the Remote Patient Intelligence Company, and a world-renowned expert on healthcare population analytics and the role of expert systems in the clinical decision process.