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Background To reduce 30-day HF readmissions and provide transitional support after hospital discharge, Intermountain Healthcare (IH) partnered with CipherHealth (CH) to create an automated HF follow up call program (HFCalls). We sought to determine if HFCalls coupled with nursing escalation algorithms improved readmissions, to identify patient themes requiring nurse interventions, and to determine if this approach was acceptable to patients. Methods Beginning November 2016, IH conducted a staged roll-out of HFCalls after a HF hospitalization. Three calls were scheduled at 48 hours, 7 - and 21 days post-discharge. HFCalls were used for patients identified by IH's automated HF Identification and Risk Report and were designed to identify readmission risk factors by focusing on patient education specific to medications, activity, weight, diet, and symptoms (MAWDS). Patients who indicated issues to follow-up questions were routed automatically to nurses with IH's “Health Answers” team, who used internally developed and vetted escalation algorithms for prompt resolution. A multidisciplinary team reviewed the escalation process and documentation of patient issues and actions on a biweekly basis. The program was evaluated over a 12-month period. Results A pilot period of 6 months, led to refinement of the process prior to a staged rollout with full implementation and standardized workflow across the IH system by November 2017. Of the patients (n=1,167) who received HFCalls during January 2017 - December 2017, 79% (n=922) answered the call and 57.2% (n=668) engaged by answering the first question. The readmission rate among those who engaged was 9.3% (n=62), compared to 13.8% (n=69) among the unengaged (p = 0.01). Themes of escalated calls varied by the 3 timed calls: call 1- issues with symptoms, medications, weighing and follow up; call 2- low salt diet/fluid restriction and, follow up; call 3- issues with activity and medications. Overall, 66% of engaged patients were satisfied with HFCalls. Conclusion Integrating automated HFCalls with nursing escalation algorithms after HF hospitalizations was associated with a lower hospital readmission rate in a manner acceptable to patients. These results show the importance of access to nursing support after discharge and reveal how patient themes may be used to guide HF patient education. The HFCalls technology may be applicable to other chronic disease states, but this requires further study.

Background Patient education is essential for impacting HF quality indicators, however is limited by typical passive, paper-based approaches. Simulation-based education, SBE (experiential, scenario-based learning) is highly effective in clinicians but underused in patients. We sought to: 1) determine feasibility of SBE in HF patients, 2) explore its effect on patient-reported outcomes (PRO); quality of life and self-care. Methods A prospective, pre-posttest, qualitative design studied a convenience sampling of consented patients admitted with HF to two urban hospitals. Within 2 weeks of discharge, SBE was performed in groups focusing on 3 scenarios: self-monitoring, HF medications, HF diet. SBE occurred in a home-like environment, in-center (IC) or through virtual telehealth (VT), allowing two-way, interactive video connection from home. Feasibility was determined by patient experience, events in SBE, and costs. Subjects completed the Kansas City Cardiomyopathy Questionnaire (KCCQ) and Self-care Index for Heart Failure (SCHFI) questionnaires prior to discharge and 1 month after SBE, when evaluation of their experience occurred. Changes in KCCQ and SCHFI were analyzed. Results 51 patients were consented, age 63.95± 13.28 yrs, 64.7% male, HFrEF and HFpEF (53% vs. 47%, respectively), 40 completed SBE; 18 via IC and 22 through VT. 21 family caregivers were present. Patient experience themes were positive; who either agreed or strongly agreed the topics were relevant (n=38, 97.4%), increased their consistency following self-care (n=36, 92.4%), and were timely after discharge (n=33, 84.6%). They recommend this type of training to other HF patients (n=37, 94.9%). The average session duration was 146 +/- 21 minutes, average cost was $304 +/- $138 per participant. Issues identified with feasibility related to scheduling and technology, with no patient safety events. Observed 30-day readmissions were 7.5% with no 30-day mortality. Both KCCQ and SCHFI increased after SBE (n=39) with a significant magnitude of change shown by others to impact outcomes, figure. Improvements were consistent in both IC and VT simulation (IC p Conclusions This novel use of SBE methodology in person or by virtual telehealth was feasible, well received by HF patients and was effective to educate and engage them in their care. Additionally, it was associated with significant improvements in PROs that are known to impact HF quality metrics. Further validation in a broader HF population would help better determine its cost-effectiveness and impact on morbidity and mortality outcomes.

Background: Many predictors to assess risk of heart failure (HF) readmissions have been suggested, yet a broad-based approach to using models is lacking. The purpose of this analysis was to use clinical predictors for readmissions found in a univariate analysis in an urban, tertiary care center to create and propose a clinically useful model for HF readmissions derived from a multivariate analysis. Methods: The enterprise data warehouse and the database registry of the Intermountain Heart Collaborative Study were queried for patients discharged from Intermountain Medical Center with a primary diagnosis of HF between January 1, 2012 and December 31, 2012. Assessment of 30-day all-cause (AC) readmissions extended through January 31, 2013. Patients were grouped into those who were, or were not, readmitted within 30 days of discharge. In a previous parallel study, we considered 71 variables in a univariate analysis, 25 of which reached statistical significance for readmissions. Themes from these variables included demographic and clinical data, clinical treatments, comorbidities, and measures of acuity. These variables were then used to perform a multivariate logistic regression analysis to create a risk prediction model for 30-day AC readmissions. Results: Of 586 eligible HF patients identified, 103(17.6%) were readmitted within 30-days; mean time to readmission 13.5 days ( 8.7). The multivariate analysis resulted in 5 variables that remained significant and were predictive of 30-day AC readmissions: longer length of stay, other admissions within the prior year, lower systolic blood pressure on admission, use of pressors or inotropes, and ever having a diagnosis of pneumonia. The final model showed good discrimination for 30-day all-cause readmission with an area under the curve c-statistic of 0.75, see Figure. Conclusions: Given national pressures to improve HF care and reduce readmissions, assessing variables associated with an increased risk in a meaningful manner is not only timely, but of clinical importance. Defining areas of higher acuityseen in longer lengths of stay and previous hospital encounters -should trigger heightened awareness of risk. Creating useful predictive models that may be tested and validated in larger populations will be the next step in making a difference in HF care.