When a patient is discharged from the hospital, being readmitted is just about the last thing he or she wants. Not only does a readmission carry with it implicit medical stressors, but there are a multitude of family, community, social, economic and quality-of-life implications. While some readmissions can neither be anticipated nor prevented, readmissions that are “potentially avoidable” are those which could be mitigated with good care transitions.
By some estimates, nearly 20% of Medicare beneficiaries who were hospitalized in the early 2000s were readmitted within 30 days.1 And nearly 34% were readmitted within 90 days. These readmissions were associated with over $17 billion in potentially avoidable costs.
Many indicators since the advent of the Hospital Readmission Reduction Program (HRRP) and its associated hospital readmission penalties suggest that readmission rates have had a modest downward trend.2 But arguably, we have a long way to go. While hospitals have responded to initiatives like the mandated Comprehensive Care for Joint Replacement (CJR) program, implementing various methods to track their patients through a 90-day post-discharge period, there remain hidden actors in the readmission problem that limit the control and influence that providers and hospitals have over the very matter for which they are being held accountable.
Lag and leak
Lag represents the time between when events occur and when hospitals become aware of those events. In other words, the time involved for that feedback to come back to the hospitals where it can be learned from and acted upon. Since CMS’ reconciliation of readmissions and penalties through programs such as HRRP occurs on an annual basis, hospitals have little real-time or near-real-time insights into readmissions when they happen.
One way in which hospitals can get more timely feedback about readmissions is through their own internal reporting systems, capturing information about patients as they are readmitted. Unfortunately, these systems are limited in that they are blind to readmissions that occur at facilities other than the index facility (or at hospitals that are outside of an integrated healthcare system that shares clinical reporting tools). This is what is meant by “leak.” Readmissions to hospitals other than the index facility often go unknown to the index facility until annual CMS reconciliation. And hospitals are at risk under HRRP for readmissions that happen across the street, across town or across the country.
The implication is that what hospitals don’t know, can hurt them. And by the time they know about the leakage, it’s too late to do anything about it in a timely manner. Iteration happens only once per year, so the system learns slowly. Certainly, lag and leak are large blind spots. But how big of a problem are they really?
Leakage: A problem of large proportions
With its annual reconciliation, CMS provides hospitals with a report that does enable hospitals to determine the degree of leakage by examining where patients have been readmitted. While those data are private because the dataset contains protected health information, researchers have also examined the degree to which leakage is a problem, and have done so in a context that is broader than that for CMS beneficiaries alone.
Dushey et al,3 for example, surveyed 3,278 patients who underwent total knee arthroplasty procedures between May, 2007 and February, 2009, paying special attention to those who experienced complications such as pulmonary embolism, deep vein thrombosis or a major bleeding episode afterward. Similarly, Greenbaum et al4 examined complications among 3,976 patients following total hip arthroplasty between May, 2007 and December, 2008. They found that in the 6 months after discharge:
- 45.5% of patients who experienced complications after total knee arthroplasty were diagnosed and treated for those complications at a different facility from the one where they had the original (index) procedure.
- 64.9% of patients who experienced complications following total hip arthroplasty presented to outside institutions for those complications.
The revisit rates for emergency room visits paint a similar picture. In examining emergency room visit and revisit rates across six states between 2006 and 2010, Duseja et al5 found that:
- Nearly a third of patients who sought emergency room care within three days of the initial visit did so at a different facility.
- Within a month of the first emergency room visit, 20% of patients went on to seek emergency care again, with 28% of those patients seeking care at a different facility.
What solutions are at our fingertips?
Increasingly, hospitals around the country have been implementing a variety of programs to help ensure good care transitions and mitigate potentially preventable readmissions. Some of these approaches, such as frequent telephone follow up until a discharged patient has a post-discharge appointment with a healthcare provider, are highly human resource intensive and can be difficult to scale. To deal with the scalability issue, risk-stratification is sometimes used to identify patients with higher readmission risk, and then support those patients with greater outreach.
Other approaches, which are not as human resource intensive, are more scalable and do not require risk-stratification for deployment, are interactive voice response (IVR) systems to automate phone call outreach to patients and trigger notifications to healthcare members when a patient provides a response that is concerning, and approaches that leverage the increasing use of email and digital tools among both young and older populations,6 using automated digital patient engagement platforms to send patients check-in emails containing guidance and remote monitoring.7
These approaches aim to help with the care transition, guide patients towards optimal recovery and monitor for those who are beginning to encounter difficulties. By way of example, when our customers use an automated digital patient engagement solution that combines remote guidance and monitoring, they have reported relative decreases in 30-day readmission rates ranging of 16% to 70%. But perhaps of equal or greater relevance is that with continuous remote monitoring, leakage can be minimized because care teams are aware of emerging clinical problems often before the patient has presented to the index or to a secondary facility. This gives the care teams real-time opportunity to guide the patient either back to the index facility (sometimes not even requiring an emergency room or inpatient encounter), or to a non-index facility if that would be more prudent.
But if a patient does get readmitted to a secondary facility, digital patient engagement tools facilitate near-real-time reporting of the readmission back to the care team of the index facility. One example of this, facilitated through 30-, 60-, or 90-day post-discharge digital surveys is to simply ask the patient. Not only are we doing this, but there is good precedent for this approach. Researchers such as Harrold et al8 have demonstrated that patient self-report of readmissions can be quite accurate. So, imagine implementing a system ranging from an automated digital patient engagement platform where these questions are asked of patients, to an analog system such as a paper survey sent in the mail, simply asking whether they have had a readmission. Now, the faucet on both the lag and the leak begins to close, and hospitals can gain more control of their outcomes, and can iterate, learn and improve more rapidly.
Patient engagement is a critical key to better health outcomes, and an extremely powerful tool in the implementation of value-based care. When information flows seamlessly between patients and care teams, in a manner that is convenient and workflow-compatible, healthcare providers can understand patients better, and healthcare administrators can develop a cycle of rapid learning to improve outcomes. The good news is that impactful patient engagement is taking place today. It’s not just an idea. It’s already reducing readmissions and lowering the barriers that have made lag and leak blind spots for hospitals.
Quality will always be an important component of healthcare, particularly as we move from a fee-for-service to a value-based reimbursement model, and implementing new and innovative ways for good care transitions will continue to help solve this very important piece of the healthcare quality puzzle.
- Jencks SF, Williams MV, Coleman EA. Rehospitalizations among Patients in the Medicare Fee-for-Service Program. N Engl J Med. 2009;360(14):1418-1428. doi:10.1056/NEJMsa0803563.
- Zuckerman RB, Sheingold SH, Orav EJ, Ruhter J, Epstein AM. Readmissions, Observation, and the Hospital Readmissions Reduction Program. N Engl J Med. 2016;374(16):1543-1551. doi:10.1056/NEJMsa1513024.
- Dushey CH, Bornstein LJ, Alexiades MM, Westrich GH. Short-term coagulation complications following total knee arthroplasty: a comparison of patient-reported and surgeon-verified complication rates. J Arthroplasty. 2011;26(8):1338-1342. doi:10.1016/j.arth.2010.11.007.
- Greenbaum JN, Bornstein LJ, Lyman S, Alexiades MM, Westrich GH. The validity of self-report as a technique for measuring short-term complications after total hip arthroplasty in a joint replacement registry. J Arthroplasty. 2012;27(7):1310-1315. doi:10.1016/j.arth.2011.10.031.
- Duseja R, Bardach NS, Lin GA, et al. Revisit rates and associated costs after an emergency department encounter: a multistate analysis. Ann Intern Med. 2015;162(11):750-756. doi:10.7326/M14-1616.
- Smith A. Older Adults and Technology Use. Pew Res Cent Internet Sci Tech. April 2014. http://www.pewinternet.org/2014/04/03/older-adults-and-technology-use/. Accessed February 11, 2017.
- Steele JR, Jones AK, Clarke RK, et al. Use of an Online Education Platform to Enhance Patients’ Knowledge About Radiation in Diagnostic Imaging. J Am Coll Radiol JACR. January 2017. doi:10.1016/j.jacr.2016.11.018.
- Harrold L, Pascal S, Lewis C, et al. Patient report improves posthospital discharge event capture in total joint replacement: a novel approach to capturing all posthospital event data. EGEMS Wash DC. 2014;2(1):1107. doi:10.13063/2327-9214.1107.