Improving heart failure care and guideline-directed medical therapy through proactive remote patient monitoring-home telehealth and pharmacy integration - PubMed (original) (raw)
Improving heart failure care and guideline-directed medical therapy through proactive remote patient monitoring-home telehealth and pharmacy integration
Kimberly A Lynch et al. BMJ Open Qual. 2022 Jul.
Abstract
To address ambulatory care sensitive hospitalisations in heart failure (HF), we implemented a quality improvement initiative to reduce admissions and improve guideline-directed medical therapy (GDMT) prescription, through proactive integration of remote patient monitoring-home telehealth (RPM-HT) and pharmacist consultations. Each enrolled patient (n=38) was assigned an RPM-HT registered nurse (RN), cardiology licensed independent provider (provider), and, if referred, a clinical pharmacy specialist (pharmacist). The RN called patients weekly and for changes detected by RPM-HT, while the pharmacist worked to optimise GDMT. The RN and pharmacist communicated clinical status changes to the provider for expedited management. Process measures were the percentage of outbound RN weekly calls missed per enrolled patient; the weekly percentage of provider interventions missed; and the number of initiative-driven diuretic changes. Outcome measures included eligible GDMT medications prescribed, optimisation of those medications, and the pre-post difference in emergency department (ED) visits/hospitalisations. After a 4-week run-in period, RN weekly calls missed per enrolled patient decreased from a mean of 21.4% (weeks 5-15) to 10.2% (weeks 16-23). Weekly missed provider interventions decreased from a mean of 15.1% (weeks 1-15) to 3.4% (weeks 16-23), with special cause variation detected. The initiative resulted in 43 diuretic changes in 21 patients. Among 34 active patients, 65 ED visits (0.16 per person-month) occurred in 12 months pre intervention compared with 8 ED visits (0.04 per person-month) for 6 intervention months (p<0.001). Among 16 patients referred to pharmacist, the per cent of eligible GDMT medications prescribed increased by 17.1% (p<0.001); the number of patients receiving all eligible medications increased from 3 to 11 (p=0.008). Similarly, the per cent optimisation of GDMT doses increased by 25.3% (p<0.001), with the number of patients maximally optimised on GDMT increasing from 1 to 6 (p=0.06). We concluded that a cardiology, RPM-HT RN and pharmacist team improved prescription of GDMT and may have reduced HF admissions.
Keywords: chronic disease management; nurses; pharmacists; quality improvement; telemedicine.
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
Conflict of interest statement
Competing interests: None declared.
Figures
Figure 1
(A) Weekly percentage of outbond RPM-HT RN calls with HF template missed per enrolled patient over time—P chart. Averaged across all intervention weeks, the mean weekly percentage of outbound RPM-HT RN calls with HF template that were missed per enrolled patient was 14.7%. (B) Weekly percentage of outbond RPM-HT RN calls with HF template missed per enrolled patient over time—P chart split mean. We split the mean to examine changes from PDSA Cycles implemented to decrease RPM-HT RN calls missed. Weeks 1 through 4 were a run-in period, so we calculated a mean for weeks 5 through 15 (RN PDSA 1) and split this from the mean for weeks 16-23 (RN PDSA 2). Total enrolment of patients in the initiative increased over time. PDSA Cycles to decrease RPM-HT RN calls missed are annotated in the figure (RN PDSA 1 at week 4; RN PDSA 2 at week 15). Upper and lower control limits were based on three standard deviations above or below the mean incorporating the sample size for each time period. CL, control limit; HF, heart failure; PDSA, Plan–Do–Study–Act Cycle; pts, patients; RN, registered nurse; RPM-HT, remote patient monitoring-home telehealth; UCL, upper control limit.
Figure 2
(A) Weekly percentage of clinically necessary provider interventions missed over time—P chart. Across all intervention weeks, mean weekly percentage of clinically necessary provider interventions missed was 10.1%. Special cause variation was detected (noted with *) starting at week 18 with six data points all more than 1 sigma below the mean, at the lower control limit. (B) Weekly percentage of clinically necessary provider interventions missed over time—P chart split mean. We split the mean to examine changes from provider PDSA cycle 1 to decrease clinically necessary provider interventions missed. Data was split into two periods, a mean calculated for weeks 1 through 15 (baseline) and a mean for weeks 16 through 23 (Provider PDSA 1). Clinically necessary cardiology provider interventions missed were defined as where, by project lead chart review, a call to a patient was clinically warranted to address a change in vital sign of device/template question but either no intervention occurred, or no documentation of any intervention was made in the electronic medical record. Missed provider interventions were corroborated by one provider who was also a quality improvement lead. Provider PDSA cycle 1 to decrease clinically necessary provider interventions missed is shown in figure. Upper and lower control limits were based on three standard deviations above or below the mean incorporating the sample size for each time period. CL, control limit; PDSA, Plan–Do–Study–Act Cycle; UCL, upper control limit.
References
- Centers for Medicare & Medicaid Services . Ambulatory care sensitive condition (ACSC) and care coordination outcome measures included in the 2013 quality and resource use reports. Available: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/PhysicianF... [Accessed 16 Feb 2022].
- Buck C, Kobb R, Sandreth R, et al. Maximizing VA remote patient monitoring during the COVID-19 response. Telehealth and Medicine Today 2021;6. 10.30953/tmt.v6.281 -DOI
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