Meyers Primary Care Institute Reducing Rehospitalizations through Automated Alerts when Older Patients are Discharged from the Hospital: A Randomized Trial Jerry Gurwitz, Lawrence Garber, Shawn Gagne, Jennifer Tjia, Peggy Preusse, Jennifer Donovan, Abir Kanaan, Jessica Ogarek, and Terry Field Meyers Primary Care Institute A joint endeavor of Fallon Community Health Plan, Reliant Medical Group, and University of Massachusetts Medical School Thank you. It’s a privilege to participate as a presenter in this plenary session. The last time I had this opportunity was in 1987 when I was completing my first year as a geriatric medicine fellow, so this experience today makes me feel very very old. Funding: AHRQ R18 HS017203 and R18 HS017817

Financial Disclosure Information This research was funded by AHRQ. Funding: AHRQ R18 HS017203 and R18 HS017817

Background Problems with continuity of care High risk transitions – rehospitalization Hand-off and communication challenges between hospital and ambulatory setting Existing methods to overcome these problems: Labor intensive Costly Problems with continuity of care: As we all know there are abundant challenges in maintain continuity of care for all older patients. High risk transitions – rehospitalization: At no time are the challenges greater than when an older individual is discharged from the hospital. And it is widely recognized that when these transitions occur in a suboptimal fashion, that can lead to potentially preventable rehospitalization. Hand-off and communication challenges between hospital and ambulatory setting: Hand-off and communication difficulties are common occurrences as our older patients transition from the hospital back to the ambulatory setting. Existing methods to overcome these problems that have been implemented by hospitals, medical groups, and health plans are often: Very labor intensive and Quite Costly

Research Questions Can automated alerts increase the likelihood of follow-up office visits? Can automated alerts reduce the risk of rehospitalization? Can automated alerts increase the likelihood of follow-up office visits? Can automated alerts reduce the risk of rehospitalization?

Study Setting Reliant Medical Group Epic ambulatory EMR In-house medical informatics team Reliant Medical Group – a multispecialty medical group located in Central Massachusetts Epic ambulatory EMR In-house medical informatics team

The Transitional Care Intervention Automated system to facilitate information flow to primary care physicians (PCPs) about patients discharged from hospital to the ambulatory setting Includes information about: new drugs added during hospital stay warnings about drug-drug interactions recommendations for dose changes and lab monitoring reminders to support staff to schedule an office visit The Transitional Care Intervention that we developed: Automated system to facilitate information flow to PCPs about patients discharged from hospital to the ambulatory setting Includes information about: new drugs added during hospital stay warnings about drug-drug interactions recommendations for dose changes and lab monitoring reminders to support staff to schedule an office visit

Required Information Elements Discharge notification Admission, discharge, transfer registration (ADT) interface Scheduling Info EMR integrated scheduling system Information is linked to data in the EMR database. Program applies rules to construct messages and direct their flow. New Meds EMR pre-discharge Claims post-discharge The required elements for this Transitional Care Intervention included: Information about when a patient was discharged from the hospital Scheduling information Information about new medications at the time of hospital discharge Laboratory monitoring information All this information needed to be linked in the EMR database in order for messages to be constructed and alerts sent. Lab Monitoring Lab results interface

Locally produced interface engine distributes messages Message Flow Primary Care Provider Locally produced interface engine distributes messages Support Staff This locally produced interface engine distributed messages to the Primary Care Provider and the PCP’s support staff.

Message Example: Dose Warning Here is an example of a alert sent to a Primary Care Provider. This relates to a patient newly initiated on lisinopril with reduced kidney function, suggesting reconsideration of the dose of lisinopril and the need to monitor kidney function closely.

Message Example: Request to Schedule an Appointment This is the type of message that would go to the Primary Care Provider’s support staff indicating the need for a visit with the PCP within one week of hospital discharge.

Technological Resources Required Linkages to hospitals and outside labs Scheduling system integrated within the EMR Real time access to claims for dispensed drugs Locally written interface engine application EMR with a flexible database Internal informatics expertise HIT-experienced physician leader The Technological Resources required to pull this off were substantial as I’ve implied: -Linkages to hospitals and outside labs -Scheduling system integrated within the EMR -Real time access to claims for dispensed drugs -Locally written interface engine application -EMR with a flexible database -Internal informatics expertise -HIT-experienced physician leader

Methods Fallon Community Health Plan Senior Plan Members Cared for by Reliant Medical Group Discharged from Saint Vincent Hospital to home -Fallon Community Health Plan Senior Plan Members -Cared for by Reliant Medical Group -Discharged from Saint Vincent Hospital to home

Methods Randomization at the time of hospital discharge One-year intervention period beginning in August 2010 -Randomization in an automated fashion at the time of hospital discharge – actually without knowing whether the patient had survived the hospitalization or where they were going to be discharged to… -One-year intervention period beginning in August 2010 -3661 discharges

3661 study discharges 5077 discharges (2563 Intervention, 2514 Control) 91 discharges Died during hospitalization 4986 discharges Discharged to nursing home, skilled nursing facility, short-term rehab, or transfer to another inpatient facility 1302 discharges 3684 discharges 23 discharges Hospitalization could not be confirmed 3661 study discharges (1870 Intervention, 1791 Control) The system that we developed randomized 5077 discharges. But the system did not know the status of the discharges. 91 of these patients died during the hospitalization. 1,302 were discharged to a NH, SNF, Rehab, or another hospital. And there were a very small number of patients for whom the hospitalization could not be absolutely confirmed. That left 3,661 study discharges: 1870 in the intervention group and 1791 in the control group.

Characteristics of Discharges in Intervention and Control Groups Intervention n=1870 Comparison n=1791 p-value Mean age 79.0 79.1 % female 52.9 52.0 0.5613 Charlson score 1 2 3 4+ 6.4% 21.6% 18.6% 17.5% 28.0% 6.6% 19.1% 20.6% 15.9% 30.2% 0.1061 Length of stay 1 day 2 days 3-4 days 5+ days 26.7% 26.6% 28.3% 18.4% 26.1% 23.8% 31.6% 18.5% 0.1150 Randomization worked very well. Mean age Gender Charlson Score Length of Stay

Results: Office visits within 7 days of hospital discharge Intervention arm: 24.2% Comparison arm: 25.1% RR 0.96, 95% CI 0.86 to 1.1

Results: Office visits within 30 days of hospital discharge Intervention arm: 62.9% Comparison arm: 62.9% RR 1.0, 95% CI 0.95 to 1.1

Results: Rehospitalizations within 30 days of hospital discharge Intervention arm: 18.4% Comparison arm: 19.5% RR 0.93, 95% CI 0.79, 1.1

Conclusions This EMR-based intervention was not effective in increasing the percentage of hospital discharges of older patients that were followed by timely office visits to primary care providers or in reducing the percentage with rehospitalization. Follow-up office visit rates were fairly high in both the intervention and comparison groups, but rehospitalization rates were also high.

Discussion: Post-hospital Disharge Office Visits with PCP Are they effective in reducing rehospitalization rates? Clogging already clogged schedules further reducing access Potential for redundant follow-up visits (e.g., cardiologist and PCP) Push-back from PCPs Push-back from patients and families Don’t forget the costs Are they effective in reducing rehospitalization rates? Clogging already clogged schedules further reducing access Potential for redundant follow-up visits (e.g., cardiologist and PCP) Push-back from PCPs Push-back from patients and families Don’t forget the costs

What’s next? Technology is not the answer to everything. Identify patients at highest risk and greatest need Which patients need a follow-up visit in 24-72 hours? Which patients can wait a week? Which patients can wait 2-4 weeks? Accountability - staff and providers – response to alerts Patient and caregiver engagement - use of patient portals Technology is not the answer to everything. Identify patients at highest risk and greatest need Which patients need a follow-up visit in 24-72 hours? Which patients can wait a week? Which patients can wait 2-4 weeks? Accountability - staff and providers – response to alerts Patient and caregiver engagement - use of patient portals