Closing the Readmission Management Loop: Stratify Risk to Tailor Intervention Intensity

November 2011

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Reducing readmission rates is a national priority with deep implications for improving patient care and reducing costs. For hospitals, readmission rates directly influence their reimbursement by the Centers for Medicare & Medicaid Services (CMS). In 2005, over 17% of hospital patients were readmitted within 30 days of discharge, resulting in $12 billion of preventable costs to Medicare alone.¹ In particular, patients suffering from congestive heart failure are vulnerable to being readmitted resulting in high costs per case.^2,3,4 Heart failure (HF) affects over 5 million Americans and accounts for $25-$35 billion in annual healthcare cost.⁵ CMS will begin imposing penalties across the board beginning in 2013 for a hospital’s ability to manage Heart Failure readmissions relative to the national average driving an increased focus on driving down readmissions. There are numerous interventions that have demonstrated reductions in readmission rates including heart failure readmission rates. However, current readmission management strategies are limited by their lack of scalability, the critical mass of professional resources required for implementation and their generic versus tailored patient-specific interventions.

Current Readmission Reduction Interventions

Various care transition intervention (CTI) models such as The Transitional Care Model⁶, Care Transition Intervention⁷, Project RED⁸, and Project BOOST⁹, have demonstrated reductions in readmission rates, including those for HF. Park Nicollet Health Services in Minnesota, for example, reduced its heart failure readmission rate from close to 20% to under 4% by adopting care transition interventions.¹⁰ A meta-analysis study conducted in 2004 concluded that patient education programs could reduce readmission rate by 21% at 6 to 9 months after discharge.¹¹ More comprehensive interventions conducted after that study have demonstrated that as much as 80% of HF readmissions can be prevented through better patient education, patient engagement, and disease management programs.^{5,12,13,14,15} The evidence in those care transition programs are so strong that, in 2011, CMS rolled out a $500M program to implement CTI-based interventions in many hospitals across the nation.

The patient discharge process and patient follow-up after discharge are two key components shared by current care transition interventions. Post-discharge patient follow-up allows the healthcare provider to capture early warning signs through patient reported outcomes; ensure compliance with critical care plan elements such as office visits with cardiologists; and to help the patient engage in better self-care behavior. The aforementioned Park Nicollet example highlights the effectiveness of a follow up system based on early detection of warning symptoms. Traditional care transition interventions typically require healthcare professionals (nurses, pharmacists, or health coaches) to perform the follow up post-discharge. However, the sheer number of professional resources required and productivity of these professionals creates a potential limitation to the scalability of the interventions. Estimates show that to prevent just one readmission, a consistent level of treatment effort is needed for nine patients.¹¹ The scalability and long term cost effectiveness of current readmission intervention programs could be potentially improved with analytical support.

Who’s At Readmission Risk?

To date, healthcare providers’ abilities to predict unplanned readmissions are poor. A large meta study in 2008 has concluded that no existing statistical model, including the CMS model, can sufficiently predict readmission risk.¹⁶ Existing models are based on multivariate regression of administrative and clinical records. However, it has been demonstrated that clinical records add little value to the prediction model––in fact, adding clinical records to administrative claims data has not resulted in any statistically significant improvement over models built with claims data only.¹⁷ While studies have indicated that the prediction algorithms can be improved by incorporating socioeconomic markers, they still only provide moderate level of predictability.¹⁸ Outside of statistical models, it has been demonstrated that clinicians’ intuitions and “gut feelings” are poor predictors of readmission risk.^19,20

Given the extensive research that has been conducted in this area, as referenced above, an alternative approach that incorporates a risk stratification model may be warranted. Risk stratification could help healthcare providers tailor intervention intensity (e.g., follow-up frequency, home visit versus phone call, and nurse versus coach) for each individual patient. In this scenario, patients with high readmission risk would be monitored more closely and receive more intensive follow-up than patients who prove to be engaged and responsive.

The Role of Patient Behavior Markers & Self-Reported Outcomes

In a clinical trial conducted by the National Cancer Institute, researchers concluded that, “Patients generally reported symptoms earlier and more frequently than clinicians. Longitudinally collected clinician CTCAE assessments better predict unfavorable clinical events, whereas patient reports better reflect daily health status. These perspectives are complementary, each providing clinically meaningful information.”²¹ A 3-year UK study on drug adverse effects concluded that, “Reports from users and relatives – especially with respect to behavioral effects – communicated information that professional reporters can never be expected to provide. They were far richer, and described suicidality and withdrawal symptoms much more clearly and intelligibly than [clinician reports].”²²

The patient’s ability to detect symptoms and seek timely care have been linked to improved outcomes for heart failure. A literature study in 2010 indicated that there are hours to days of delay from symptom onset to hospital admission of heart failure due to patient’s failure to recognize warning signs, resulting in poor outcomes.²³ The patient’s engagement in his or her own care can be measured via the Patient Activation Measure (PAM).²⁴ A controlled longitudinal survey of 476 patients has been demonstrated that patient activation level is strongly correlated with better self-management and care utilization in patients with chronic diseases.²⁵ Increased patient activation has also been associated with better post-surgery rehabilitation behaviors.²⁶

A Closed Loop Patient Centric Solution

Innovative healthcare technology companies such as Ringful Health™ have developed new readmission management solutions that can potentially supplement and enhance existing intervention programs by incorporating patient behavior markers in a risk stratification model for customizing patient specific intervention. Five key components of such readmission management solutions include:

• Automated Patient Engagement for Pervasive Behavior Collection and Intervention: This component supplements care transition programs already in use and enables pervasive behavior data collection and intervention methods such as the ability to capture patient behavior markers, like data reporting patterns, timely follow-up appointments with cardiologists and response to telephone follow-up calls. The pattern of timely reporting of self-monitoring data has been demonstrated to correlate with better self care behavior among diabetes populations (Shah & Manuel 2008). Follow up with cardiologists and response to telephone follow ups are known predictors of low readmission rate.^27,28 Automated patient engagement solutions interact with patients with two-way reminders, daily journals, and patient assessment tools and utilize validated patient reported outcome tools for the web and consumer mobile devices. Questionnaires and reminders are delivered to patients via telecommunication channels they already use, such as text messaging, email, web, and mobile web.
• Advanced Workflow Management & Escalation Intervention Software: Workflow management software and rules engines are required to manage automated patient interactions and trigger escalations to provide more intensive clinician-based interventions for patients identified with high risk or early warning signs and leverages interventions from traditional care transition programs such as TCM, CTI, and ProjectRED.
• Data Analytics Engine and Risk Stratification Model: A regression model that analyzes patient behavior markers, self-reported outcomes, socioeconomic data and existing clinical data to stratify patient readmission risk is a critical component of a readmission management solution. This includes an analytics engine that collects data from the automated patient engagement solution, as well as clinical and socioeconomic data maintained by the hospital and physician practice. The data collected includes daily answers to symptom and vital sign questionnaires, answers to PAM questionnaires, compliance with assessment and follow up protocols, response to reminders, and nursing escalation needed to engage the patient.
• Low Adoption Barriers Using Low Cost Consumer Technology: An effective solution which involves the patient’s active participation requires low barriers to adoption. Leveraging consumer technology (such as mobile devices) that is already used on a daily basis by patients of all income levels to collect and analyze behavior and outcomes data is key.
• 100% Patient Coverage: The solution should cover 100% of discharged patients at a low cost and with high reliability.

Conclusion

Preventable hospital readmission rates are an important measure of healthcare quality. Evidence-based care transition models have demonstrated effectiveness in lowering readmissions; however, such programs have challenges in scalable and cost-effective implementation. Consider readmission management solutions such as those from healthcare technology companies like Ringful Health which integrate with existing intervention programs, incorporate patient behavior metrics through low cost screening, identify high risk patients and demonstrate a complete loop from data collection, analysis and prediction, to tailored interventions (i.e. “the loop of evidence”).

Steve Andrade is the President and founder of TrueWind Advisors, LLC. With over 20 years of operating experience as a CEO, GM and business leader, Steve has successfully led the growth and innovation at early stage, medium-sized and large global corporations in the technology, life sciences and healthcare industries. He is an independent director and strategic advisor at several healthcare technology and life science organizations.

Michael Yuan, Ph.D., is the CEO and founder of Ringful Health, Inc. a healthcare technology company dedicated to helping hospitals improve reimbursement rates by improving core quality metrics, such as HCAHPS scores and readmission rates.

References

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