1. 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 HS and R18 HS017817

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

3. 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

4. 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?

5. 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

6. 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

7. 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

8. 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.

9. 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.

10. 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.

11. 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

12. 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

13. 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

14. 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: in the intervention group and 1791 in the control group.

15. 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

16. 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

17. 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

18. 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

19. 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.

20. 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

21. 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 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 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