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Home based chronic disease care with digital health monitoring for value based healthcare

by | Jul 18, 2021

Healthcare systems are looking for ways to not only decrease spending but increase value of care.  Implementing a dual model of home-based chronic care services with digital health monitoring accomplishes both decrease in spending and promotes goal directed, value-based care across an ACO population.

Center for Medical and Medicaid Innovation

The Center for Medicare and Medicaid Innovation (CMMI) was authorized under the Affordable Care Act and tasked with designing, implementing, and testing new healthcare payment models to address concerns about  rising costs, quality of care, and inefficient spending.[1]  The CMMI has launched over 40 new payment models.  Of the models, the Accountable Care Organization model of reimbursement is the most successful.  The net savings to Medicare totaled $47 million in 2016 relative to benchmark spending levels.  Some are proposing to implement the ACO model nationwide. 

Home based care

The implementation of comprehensive home-based health care is a proven method for healthcare cost savings in an ACO [2].  In fact, Medicare supports comprehensive primary care services at home through the Independence at Home Program.  This program promotes in home care for chronically ill individuals with functional limitations.  Home based palliative care programs are positioned to augment these programs through symptom management of chronically ill patients in their homes.   Palliative care and home based primary care align with the goal directed value based healthcare[3].  Goal directed care places greater emphasis upon patient centered goals and meaningful outcomes.  This includes greater focus upon prevention, meaningful physician patient relationships and reframing health directed goals to improve quality and reduce costs.

Remote monitoring of heart rate variability

While counterintuitive to the high touch, high contact healthcare.  Implementing technology such as remote patient monitoring also serves to enhance the provider-patient relationship.  Remote patient monitoring, closely supports the 4Ps of healthcare: personalized, participatory, predictive, and preventive.[4]  Wearing biosensors allows patients to actively participate in their care while experiencing personalized management through close care provider interaction.  Heart rate variability (HRV) is one easily trackable and manageable metric that is directly linked to longevity.  Trends in individual HRV is reflective of autonomic nervous system health, blood pressure, myocardial infarction risk, diabetes control, renal disease, tobacco and alcohol use and sleep[5].  Monitoring HRV provides a highly personalized approach to patient monitoring and management in conjunction with home-based care.  The HRV measurement provides a predictive measurement that considers the risk of disease and resilience factors to help promotion and maintenance of optimal health and well-being.  The preventive approaches focus upon early identification and intervention to eliminate onset or severity of disease states and promote health.  Early detection enhances early intervention and cost savings.

An optimal level of HRV is associated with health, self-regulatory capacity, adaptability or resilience.[6]  A low heart rate variability implies either excess sympathetic (flight or fight) heart stimulation or excess parasympathetic stimulation (rest and digest).  Following the trends of heart rate variability in individuals of a population can provide a useful tool in the management of individuals and populations.  Drury et al [7] suggest using a wearable high fidelity Oura sensor ring to acquire HRV, in addition to physiological indicators, to track pre-illness population at baseline and ongoing longitudinal Community assessment of disease indicators. 

Combining high touch with high technology for cost containment

By combining the high touch, in-home healthcare with remote, continuous digital patient monitoring, healthcare providers gain an accurate snapshot of the health status of their patients.  This data helps healthcare providers optimize the health of individual patients and prevent readmissions. Healthcare systems can also monitor the overall status of their ACO population to implement additional system-wide cost containment measures.

1.            Foundation, H.K.F., What is CMMI and other FAQs about the CMS Innovation Center. 2018.

2.            Lustbader, D., et al., The Impact of a Home-Based Palliative Care Program in an Accountable Care Organization. J Palliat Med, 2017. 20(1): p. 23-28.

3.            Mold, J., Goal-Directed Health Care: Redefining Health and Health Care in the Era of Value-Based Care. Cureus, 2017. 9(2): p. e1043.

4.            Drury, R.L., Unraveling health as a complex adaptive system: data mining, cloud computing, machine learning and biosensors as synergistic technologies. International Journal of Genomics and Data Mining, 2017. 105(10).

5.            Rajendra Acharya, U., et al., Heart rate variability: a review. Med Biol Eng Comput, 2006. 44(12): p. 1031-51.

6.            Shaffer, F. and J.P. Ginsberg, An Overview of Heart Rate Variability Metrics and Norms. Front Public Health, 2017. 5: p. 258.

7.            Drury, R.L., et al., Wireless Heart Rate Variability in Assessing Community COVID-19. Front Neurosci, 2021. 15: p. 564159.

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