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Using Care Management Tech to Match and Motivate Patient Cohorts

>>Using Care Management Tech to Match and Motivate Patient Cohorts

Motivation Drives Us to Act on Our Goals

Care Management technologies must tap into patients’ intrinsic and extrinsic motivation to support healthy choices and improve their quality of life.  Here, we highlight modern techniques and technology that support motivated care management as part of population health and cohort-centric care. 

Matching and Motivating Patients through Care Management Technologies

Motivation is the drive to act on a set of goals. These goals may be ours or someone else’s; the drive may come from within (intrinsic motivation) or from an external source (extrinsic motivation). Since motivation varies from person to person in degree and for different things, to improve patient compliance, we need to understand each individual’s motivational traits and create a personalized action plan to satisfy those traits.

Ideally, intrinsic motivation is preferred as the patient satisfies an innate psychological need while performing tasks that they find interesting or enjoyable. But tapping into intrinsic motivation can be challenging, especially in patients with chronic illnesses, who may be overwhelmed with the magnitude of life change and medical appointments they face. In these cases, extrinsic motivation can be highly effective if it reflects a patient’s fundamental need for competence, autonomy, and connectedness (Ryan and Deci, 2000). Motivation shaped on intrinsic values will enable the patient to stick to his or her care plan because they have made the plan through their free-will guided by a health professional. Patients adhere to the plan because they believe that the plan is going to improve their quality of life. Moreover, their trusted relationship with their health professional makes the patient feel respected and valued.

Recognizing this need, clinical psychologist William Richard Miller and his team developed a new patient-first care model approach called Motivational Interviewing (MI). (Miller, 2012) MI is client-centered and goal-oriented, to help patients set actionable, realistic, and incremental goals, and help them to achieve them. MI techniques call for effective communication that establishes a collaborative tone, avoids arguments, and provides a safe and non-judgmental platform to ask evocative questions.

Care Manager with patient

Motivational Interviewing and Care Management

In a care management setting, a care manager (or care coordinator or peer specialist, depending on the circumstance) acts as an intermediary between the doctor and the patient, assessing the patients’ needs and creating action plans. Used effectively, MI enables the care manager to ensure that the care plan is highly personalized, leverages intrinsic motivators, and is tailored to the patient’s change journey: when the patient is contemplating a change, actively making the change, or maintaining the change.

Perhaps the most compelling evidence of MI’s success comes from the landmark Cardiovascular Health Initiative and Lifestyle Education (CHILE) study. (Scales et al., 1997) In this study, 61 patients were split into two groups, and one group was given traditional rehabilitation treatment while the other was given MI along with the conventional treatment. After the program, the patients in the latter group were “exercising at energy expenditures equivalent to 7.5 hours per week of brisk walking compared with 3.5 hours per week in the control group.” Studies have proven that patients who exercise at these higher volumes (more than 4 hours per week) had a higher chance at disease stabilization or reversal. Three months post-completion, patients who were motivationally interviewed were significantly more active and consistently increased their physical activity level.

The success of MI, as established in the CHILE study and many others, primarily depends on whether a connection between the patient and health care provider, can be created quickly. This is not always straight-forward. A Harvard study, initiated in a psychotherapy setting, found that nearly half of the patients drop out of treatment before any significant progress is made, which was mainly attributed to poor interpersonal relationships between the therapist and the patient. We can compare this study to the premise of MI, where the main success factor depends on the trust and patience built between the care manager and the patient.

Healthcare is Personal. Care Management Should Be Personal Too.

Healthcare and motivation are personal. Let me share my example. Six years ago, I had a motorcycle accident. While turning a corner on a muddy road, I lost balance and suffered a ‘highside’ accident. Highside accidents occur when you fall over and toward the outside of the turn. I was flung over and on to the path of the speeding bike and suffered a slipped disc, a left shoulder dislocation, as well as skin abrasions. I spent the night in the ER and was referred to a physical therapist who is also a licensed chiropractor, specializing in motorcycle accidents.

He intrigued me with his deep understanding of the precise injuries that occurs with each kind of motorcycle accident. Within a few days, we developed a rapport, and I had great respect and admiration for him because I was confident that he knew what he was talking about in a ‘non-showy’ way. He understood my need for adventure and, in a non-judgmental way, explained how I could have prevented my injury if I had shifted my back to the right slightly and didn’t lean too much.

My experience went beyond a typical physical therapy session. He personalized the treatment plan, appealing to my interests and aspirations, and creating a plan to achieve incremental goals. I was asked to walk 15 minutes a day and to wear a left-arm band to hold my shoulder at a 90-degree angle for a month. The goal was to engage in light physical activities within two weeks and perform everyday activities with little or no assistance within a month.

Though he transferred to another branch on the opposite end of the city after the first month, he continued to treat me because he thought that my progress might slow down if a new therapist were assigned. I was able and motivated to participate in grueling physical therapies and painful chiropractic adjustments because the therapist paced the treatment, incorporating my inputs, and adapting to my needs based on my change journey.

In the end, I achieved all my physical goals and recovered enough to ride my motorcycle around the sixth month. Today, I am almost fully recovered and need only occasional chiropractic adjustments.

Technology to Support Motivated Care Management

I was lucky to find a therapist that I could build trust with quickly. One who tailored my plan, which accelerated my journey and ensured the best possible outcome.

In a care management program, technology can create this style of patient-practitioner relationship proactively and consistently, tapping into each individual’s intrinsic motivational characteristics.

Technology can help: 

  1. Pair care managers with the most appropriate cohort of patients based on a variety of factors.
  2. A patient can find the best possible match with a care manager who meets needs like personality style, schedule availability, and geographical location.
  3. The care manager can access patient information from anywhere — easily, quickly, and securely.
  4. The care manager initiates the right conversational topics and questions (“assessments”) at just the right time to build rapport, nurture the patient relationship, and personalize the care plan, thereby avoiding an inappropriate “one size fits all” approach.

The Highest Form of Healthcare: Technology-Enabled Cohort Care Management

Cohort care management can emulate the highest form of healthcare – motivated, personal, tailored, and engaging. Technology, including AI, machine learning, mobile apps, and more, can help match the right practitioner to meet the patient’s needs and personality, as well as ensure that care managers engage the patient’s motivations with the right conversations at just the right time.

To explore cohort care management technologies and our unique approach to engaging patients, contact us 

Two Scenarios: The Power of Care Management Technology

Meet Sheila: 

patient -Sheila

Sheila is an executive who has chronic cardiovascular disease. Her doctor has instructed her to change her diet and exercise regularly. The doctor recognizes her high-stress job and the difficulty she will face making the needed lifestyle changes and recommends a care management program that will match her with a care manager who has demonstrated success with people in similar situations.

The doctor can build rapport with the executive as he understands the innate competitive drive that drives her, and he leverages MI techniques to ensure productive dialogue that’s guided by standardized questionnaires. Based on her trust in the doctor, she agrees to participate, and she is thoughtfully matched with an appropriate care manager. Sheila builds respect and trust for her care manager, helping her to open up about her anxiety, which is also incorporated into the conversation.

“Nested” machine learning models – based on patient profiles, cohort patterns, and answers to questions — provide the care manager with suggested problems, goals, and actions that have proven to be effective in this patient’s cohort population.  Given her economic means, the care manager works with her to create an exercise plan under the guidance of a personal trainer who comes to her office, as well as helps her set up healthier lunch options through office food delivery.

Meet Bob

patient - Bob

Bob is a retired factory worker who has been through cardiac rehabilitation. He spent years working long hours at physically demanding work, which took a toll on his physical mobility. He lives in an urban neighborhood where he uses public transportation. His doctor (same as Sheila’s) instructed him to change her diet and exercise regularly (just like Sheila), as well as enrolled him in the same care management program.

Bob was matched with a care manager who lives in his community and has worked with many retirees in the area.  Her experience tells her that safety and affordability are critical factors in developing a realistic plan for Bob.

In his case, the machine learning models cue the care manager to focus on conversational topics around his access to grocery stores versus using convenience stores for daily food staples. She works with him to register with community services for rides to a grocery store that has a great produce department and to obtain vouchers that can be used at the nearby farmer’s market in good weather. He is encouraged to ask a couple of neighbors for help with transportation when needed. He is even encouraged to share his goals with his regular retiree buddies that he has coffee with to plan walking sessions at the local park.

Bibliography:

Ryan, R. and Deci, E. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), pp.68-78.

Miller, W. (2012). Motivational Interviewing, Third Edition. Guilford Press.
Scales, R., Lueker, R., Atterbom, H., Handmaker, N. and Jackson, K. (1997). IMPACT OF MOTIVATIONAL INTERVIEWING AND SKILLS-BASED COUNSELING ON OUTCOMES OF CARDIAC REHABILITATION. Journal of Cardiopulmonary Rehabilitation, 17(5), p.328.

Banu Prakash Nallani - SDLC Partners

Insight Contributor

Banu Prakash Nallani,
Associate — BI & Analytics, Business Consulting

Banu Nallani is a data scientist with a primary focus on health tech and digital marketing. He models behavior algorithms to better understand and predict human actions. He is presently working with a major IT firm to automate a large repository of financial and demand management reports. And previously worked on a pharmacy-cost-savings prediction initiative for an $18 billion health insurance firm to model ‘why’ different medications were prescribed among similar diabetic patients.

He has a Masters in Information Science from Carnegie Mellon University and Bachelor’s in Computer Engineering and Philosophy from India.