Improve the patient recovery journey by using duration data and predictive modeling

A total care journey that emphasizes a safe, but prompt recovery from an illness or injury can result in numerous positive outcomes.

However, hospitals and health system often lack the time, data and tools at the point-of-care to easily design the tailored, phased treatment plans needed to return patients to the activities they love quickly and safely – at the optimal time.

Shortening recovery times and helping patients return to health delivers both physiological and psychological benefits. For example, there is extensive research, most notably by British physician Gordon Waddell and colleagues, showing that patients who return to work after a short-term disability-causing illness or injury have faster recoveries, improved mental health as well as overall greater quality of life.1

Faster recovery also means the patient will likely consume fewer care resources, which reduces a hospital or health system’s overall costs and positively affects any risk-bearing payment. A full recovery also means that the patient is unlikely to return to the hospital for issues concerning the same procedure or illness within 30 days, which can carry a 3-percent penalty on Medicare reimbursements.2

Focusing on the total care journey, physicians can design a tailored, but evidence-based treatment plan based on patients’ unique clinical and demographic factors as well as their personal recovery goals, such as returning to work or recreational activities. Data and IT tools are increasingly available to help providers consider this more holistic question that speeds recovery, reduces healthcare spending and prevents avoidable readmissions, all of which benefit both patients and providers.

Duration tables to set tangible goals
Estimating recovery times and setting key benchmarks to plot the total care journey can be challenging. Until recently, physicians based their recovery duration estimates on their training and experience, but have been limited in their holistic view of the patient due to disparate information systems or care delivered by unaffiliated specialty providers, particularly in behavioral health.

Lately, more hospitals and health systems are taking after occupational health specialties by offering providers recovery duration tables that display the number of days it should take patients to resume their normal activities across a range of activity levels. For example, if a patient has a total-knee or total-hip replacement, two procedures subject to Medicare readmission penalties, the recovery duration tables help the patients and providers visualize when they can expect to stand on their own, walk or even run again at the optimal timeframe. Plus, these tables are backed by real clinical cases, not just subjective experiences of individual providers.

Properly designed duration tables, are based on millions of real-life cases and undergo a review process to ensure maximum accuracy and reliability. The data refinement process should include multiple levels of cleansing, as well as scientific review by a diverse and highly qualified medical advisory board.

This process is essential to ensure that providers have the most useful, trustworthy and actionable intelligence at the point-of-care, helping to consider the total care journey in their treatment plan and establish return-to-activity expectations. Patients, too, will appreciate being presented with concrete estimates to help them set goals, visualize their recovery and engage in their care by following providers’ treatment plans. Reducing their out-of-pocket costs through fewer appointments, treatments and prescriptions can also increase patient engagement.

Predictive modeling for greater precision
For an even more tailored treatment plan and recovery estimate, point-of-care predictive modeling tools are available that allow physicians to input patients’ co-existing medical conditions and other factors that may influence recovery times, such as job-related activity levels. These tools use an algorithm that allow providers to input ICD-10 codes as well as the patient’s age, gender and region of the country for a specific estimate of their return-to-activity recovery time. It also helps providers design a treatment plan that helps patients become active sooner and recover at a faster pace. As Waddell writes that management of the most common illnesses and injuries should focus on “staying active, restoring function, enabling and supporting sick and disabled people to participate in society as fully as possible.”

While managing patients toward achieving optimal recovery times highlighted in the duration tables is preferred, the predictive model can be useful to gauge recovery for patients with complications and anticipate potential obstacles to positive outcomes. Like the duration tables, predictive modeling tools would preferably be integrated into the electronic health record, to help physicians design treatment plans and share clinical decisions with patients more efficiently.

Analytics to improve performance
Using these recovery estimate tools, combined with tracking their patients’ recovery durations, hospitals and health systems can measure their performance against industry benchmarks. If patients are trending toward longer recovery times, it could indicate the organization needs to explore evidence-based clinical practice guidelines to shorten return- to-activity timeframes. Similar tools have been used in occupational health specialties for years to help return employees to work through safe and effective accommodations that phase them back into full duty.

When guidelines are utilized in conjunction with the duration tables and predictive modeling tool, hospitals and health systems will be able to truly consider the patient’s entire care journey, resulting in a safe, prompt return to the patients’ normal enjoyable activities. Through these faster recovery times and more satisfied patients, organizations demonstrate their superior care quality to employers.

As a result, the healthcare organization will be viewed among employers as the preferred provider in their market and attract more favorable payer contracts, while simultaneously investing more in patients’ recovery journeys and increasing their value-based care reimbursement.

About the author:
Joe Guerriero is senior vice president of ReedGroup’s MDGuidelines.

1 http://www.spineresearch.org.uk/media/819/is-work-good.pdf
2 http://khn.org/news/more-than-half-of-hospitals-to-be-penalized-for-excess-readmissions/

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