Other Titles
Cardiac disease health promotion
Abstract
Purpose: To implement strategies to improve care and patients" experience and reduce readmissions for heart failure (HF) patients, Ronald Reagan UCLA Medical Center accepted an invitation from the American College of Cardiology (ACC) to join the Patient Navigator Program (PNP). The goal for the program is for hospitals to establish a patient-centered focus that involves making hospitalization less stressful for patients by providing evidence-based quality improvement strategies. At the initiation of the program (Spring 2014), UCLA utilized a validated risk model, LACE index, to identify patients who are at high risk of readmissions before discharge. This tool has been used to predict the risk of unplanned readmissions as well as mortality within 30 days of hospital discharge in both medical and surgical patients.
Methods: The LACE Index tool is used to identify patients who would benefit from specific interventions. The score is calculated in the electronic health record (EHR) for each patient from 0 to 19 on the basis of all the following parameters: length of stay (L), acuity of admission (A), comorbidity (C), and emergency department visits in the preceding 6 months (E). Based on the LACE criteria, a low (0-6), medium (7-10) or high (≥11), each score has an identified bundled intervention for each level of risk (Table 1). For example, a HF patient with a low risk score of 6 would receive medication reconciliation from the pharmacist, an updated medication list from the nurse, and a standardized discharge summary from the discharging physician, as well as a follow-up appointment within 5 days. In contrast, a HF patient with a high risk score of 14 would receive the same interventions plus consultations by a physical therapist, a social worker, a case manager, and a dietician; one-to-one medication teaching by the pharmacist; and a follow-up appointment within 3 days.
Results: The LACE Index score is now calculated in the EHR for all patients. Currently, HF patients receive bundled interventions 80% of the time on the cardiac wards. Since the initiation of the risk score, 30-day unadjusted readmission rates for HF patients at UCLA have decreased from 19% (baseline) to 16.7% (2016, Q2) as compared to the Navigator hospitals 19.2% (baseline) to 17.9% (2016, Q2). In the area of patient experience related to patients" understanding of medications, UCLA is consistently higher than other Navigator hospitals (100% vs. 72.2%) and has identified and shared best practices during the monthly webinars. In addition, UCLA has increased the number of HF patients consistently receiving a follow-up appointment within 7 days after discharge: baseline of 76.6% to 87.5% (2016, Q2).
Conclusion: There are numerous factors that cause hospital readmissions. By using a risk model, UCLA is able to identify patients who would benefit from specific evidence-based interventions. This has improved outcomes in 30-day unadjusted readmission rates and patient experience.
Sigma Membership
Gamma Tau at-Large
Type
Presentation
Format Type
Text-based Document
Study Design/Type
N/A
Research Approach
N/A
Keywords:
Bundled Interventions, Hospital Readmissions, Risk Score
Recommended Citation
Dermenchyan, Anna, "Preventing heart failure readmissions by using a risk-stratification tool" (2017). INRC (Congress). 269.
https://www.sigmarepository.org/inrc/2017/presentations_2017/269
Conference Name
28th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Dublin, Ireland
Conference Year
2017
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Acquisition
Proxy-submission
Preventing heart failure readmissions by using a risk-stratification tool
Dublin, Ireland
Purpose: To implement strategies to improve care and patients" experience and reduce readmissions for heart failure (HF) patients, Ronald Reagan UCLA Medical Center accepted an invitation from the American College of Cardiology (ACC) to join the Patient Navigator Program (PNP). The goal for the program is for hospitals to establish a patient-centered focus that involves making hospitalization less stressful for patients by providing evidence-based quality improvement strategies. At the initiation of the program (Spring 2014), UCLA utilized a validated risk model, LACE index, to identify patients who are at high risk of readmissions before discharge. This tool has been used to predict the risk of unplanned readmissions as well as mortality within 30 days of hospital discharge in both medical and surgical patients.
Methods: The LACE Index tool is used to identify patients who would benefit from specific interventions. The score is calculated in the electronic health record (EHR) for each patient from 0 to 19 on the basis of all the following parameters: length of stay (L), acuity of admission (A), comorbidity (C), and emergency department visits in the preceding 6 months (E). Based on the LACE criteria, a low (0-6), medium (7-10) or high (≥11), each score has an identified bundled intervention for each level of risk (Table 1). For example, a HF patient with a low risk score of 6 would receive medication reconciliation from the pharmacist, an updated medication list from the nurse, and a standardized discharge summary from the discharging physician, as well as a follow-up appointment within 5 days. In contrast, a HF patient with a high risk score of 14 would receive the same interventions plus consultations by a physical therapist, a social worker, a case manager, and a dietician; one-to-one medication teaching by the pharmacist; and a follow-up appointment within 3 days.
Results: The LACE Index score is now calculated in the EHR for all patients. Currently, HF patients receive bundled interventions 80% of the time on the cardiac wards. Since the initiation of the risk score, 30-day unadjusted readmission rates for HF patients at UCLA have decreased from 19% (baseline) to 16.7% (2016, Q2) as compared to the Navigator hospitals 19.2% (baseline) to 17.9% (2016, Q2). In the area of patient experience related to patients" understanding of medications, UCLA is consistently higher than other Navigator hospitals (100% vs. 72.2%) and has identified and shared best practices during the monthly webinars. In addition, UCLA has increased the number of HF patients consistently receiving a follow-up appointment within 7 days after discharge: baseline of 76.6% to 87.5% (2016, Q2).
Conclusion: There are numerous factors that cause hospital readmissions. By using a risk model, UCLA is able to identify patients who would benefit from specific evidence-based interventions. This has improved outcomes in 30-day unadjusted readmission rates and patient experience.