Identifying patients with post-discharge care problems using an interactive voice response system

J Gen Intern Med. 2009 Apr;24(4):520-5. doi: 10.1007/s11606-009-0910-3. Epub 2009 Jan 21.


Introduction: Adverse events (AEs) are poor outcomes caused by medical care. They occur in 20% of medical patients following hospital discharge. We designed an interactive voice response system (IVRS) with the intent of identifying patients who might be experiencing an AE following discharge or were at risk of developing one.

Objectives: We determined the proportion of post-discharge patients requiring an intervention after identifying potential problems using the IVRS, the relationship between IVRS responses and AE occurrence, and patients' opinions of the IVRS call.

Methods: We studied patients discharged from the general medical service of an academic hospital. The IVRS called patients 2 days post-discharge and asked three questions to determine the need for nurse follow-up. We contacted patients 30 days later to elicit AE status and perceptions of the IVRS.

Results: Our cohort consisted of 270 elderly patients [median 64 years (IQR 50-76)] with multiple co-morbidities. Responses to the IVRS identified 57 patients (21%, 95% CI 17%-27%) for follow-up. When contacted by a nurse, 25 patients (9%, 95% CI 6%-13%) actually required an intervention. At 30-day follow-up, AEs occurred in 33 patients (12%, 95% CI 8%-17%). Only three AEs (9%) were identified by the IVRS; the remainder occurred before or after the IVRS call. Patients remembering the IVRS call found it easy to use (97%), and a minority would prefer a person to call (8%).

Conclusion: An IVRS-based method of monitoring was acceptable to patients and identified a significant proportion requiring changes in management. However, the method identified only a minority of AEs. To have a significant improvement in care, this method will need to be combined with other interventions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Continuity of Patient Care*
  • Feasibility Studies
  • Female
  • Humans
  • Male
  • Middle Aged
  • Monitoring, Physiologic / methods*
  • Patient Satisfaction
  • Speech Recognition Software