Home health care after discharge is associated with lower readmission rates for patients with acute myocardial infarction

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Muhammad A. Sheikh ◽  
David Ngendahimana ◽  
Salil V. Deo ◽  
Sajjad Raza ◽  
Salah E. Altarabsheh ◽  
...  
2020 ◽  
Vol 13 (Suppl_1) ◽  
Author(s):  
Muhammad A Sheikh ◽  
David Ngendahimana ◽  
Salil V Deo ◽  
Sajjad Raza ◽  
Salah Altarabsheh ◽  
...  

Objective: Home health care (HHC) is a support tool to transition patients after discharge and acute myocardial infarction (AMI) is a significant cause of morbidity and mortality in the U.S. However, little is known regarding the impact of HHC on AMI patients. We sought to identify predictors of readmissions among AMI patients, characteristics of those who receive HHC and investigate the association of HHC with readmission. Methods: We queried the National Readmission Database (NRD) (January 2012 - December 2014), to identify patients discharged after AMI and selected patients who were discharged home with (HHC+) and without HHC (HHC-). We reported national estimates with survey methods with weights provided in our data. After univariate exploratory analyses, we developed a regression model to identify the probability of each patient to receive HHC. From the propensity score, we calculated average treatment on the treated (ATT) weights. These ATT weights were included in the logistic regression model to determine the impact of HHC on readmission after adjusting for available clinical confounders. We considered post-weighting standardized differences <10% as appropriate for our ATT model. To determine clinical factors associated with readmission, we also performed a multi-variable logistic regression with readmission as the end-point. All results were reported as risk ratios (RR) with their 95% confidence intervals (CI). Results: Between January 2012 to December 2014, 406,237 patients were treated for AMI and discharged home with or without HHC. Among these 9.4% (38,215) received HHC. HHC+ patients were older (mean age 77 ± 11 vs 60 ±12 years p<0.001), more likely to be female (53.6% vs. 26.9%, p <0.001), and have cancer (3.7% vs 1.3%, p <0.001), congestive heart failure (5.7% vs. 0.5%, p <0.001), chronic pulmonary disease (23.2% vs. 12.7%, p <0.001), chronic kidney disease (26.9% vs 6.9%, p <0.001), diabetes (35.6% vs. 26.7%, p <0.001), hypertension (70.7% vs. 64.8%, p <0.001) and peripheral vascular disease (14.6% vs 6.4%, p <0.001). Patients readmitted after MI were more likely to be older and have diabetes (RR 1.42, 95% CI 1.37-1.48), CHF (RR 5.89, CI 5.55-6.26) or COPD (RR 1.59, 1.52-1.65). Unadjusted 30-day readmission rate was 20.9% for HHC+ and 8.2% for HHC- patients. Propensity-weighted adjustment for covariates yielded 36,979 HHC+ patients and 37,785 HHC- patients. Adjusted risk rations (RR) for 30-day readmission were computed using ATT weights, and HHC+ patients had significantly lower readmission risk (RR 0.89, 95% CI 0.82 - 0.96) compared to HHC- (RR 1.12, 95% CI 1.04 - 1.21; p < 0.001) Conclusion: In the United States, a small proportion of patients receive home health care after discharge post-AMI. Older, females and those with diabetes or heart failure are more likely to receive home health care. Use of home health care may be associated with lower 30-day readmission rates after AMI.


2020 ◽  
Author(s):  
Abdulaziz A Alodhayani ◽  
Marwah Mazen Hassounah ◽  
Fatima R Qadri ◽  
Noura A Abouammoh ◽  
Zakiuddin Ahmed ◽  
...  

BACKGROUND There is growing evidence of the need to consider cultural factors in the design and implementation of digital health interventions. However, there is still inadequate knowledge pertaining to what aspects of the Saudi Arabian culture need to be considered in the design and implementation of digital health programs, especially in the context of home health care services for chronically and terminally ill patients. OBJECTIVE This study aims to explore the specific cultural factors relating to patients and their caregivers from the perspective of physicians, nurses, and trainers that have influenced the pilot implementation of Remotely Accessible Healthcare At Home (RAHAH); a connected health program in the Home Health Care Department at King Saud University Medical City, Riyadh, Saudi Arabia. METHODS A qualitative study design was adopted to conduct a focus group discussion (FGD) in July 2019 using a semi-structured interview guide with 3 female and 4 male participants working as nurses, family physicians, and information technologists. Qualitative data obtained were analyzed using a thematic framework analysis. RESULTS Two categories emerged from the FGD that influenced the experiences of digital health program intervention: (1) culture-related factors including language and communication, cultural views on using cameras during consultation, non-adherence to online consultations, and family role and commitment (2) caregiver characteristics in telemedicine that includes their skills and education and electronic literacy. Participants of this study revealed that indirect contact with the patients and their family members may work as a barrier to proper communication through RAHAH. CONCLUSIONS We recommend exploring the use of interpreters in digital health, creating awareness among the local population regarding privacy in digital health, and actively involving the direct family members with the healthcare providers.


2019 ◽  
Vol 7 (4) ◽  
pp. 561-569
Author(s):  
Jo-Ana D Chase ◽  
David Russell ◽  
Meridith Rice ◽  
Carmen Abbott ◽  
Kathryn H Bowles ◽  
...  

Background: Post-acute home health-care (HHC) services provide a unique opportunity to train and support family caregivers of older adults returning home after a hospitalization. To enhance family-focused training and support strategies, we must first understand caregivers’ experiences. Objective: To explore caregivers’ experiences regarding training and support for managing older adults’ physical functioning (PF) needs in the post-acute HHC setting. Method: We conducted a qualitative descriptive study using semi-structured telephone interviews of 20 family caregivers. Interviews were recorded, transcribed, and analyzed using conventional content analysis. Results: We identified the following primary categories: facilitators to learning (eg, past experience, learning methods), barriers to learning (eg, learning on their own, communication, timing/logistics, preferred information and timing of information delivery), and interactions with HHC providers (eg, positive/negative interactions, provider training and knowledge). Conclusion: Caregivers were responsive to learning strategies to manage older adults’ PF needs and, importantly, voiced ideas to improve family-focused training and support. HHC providers can use these findings to tailor training and support of family caregivers in the post-acute HHC setting.


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