Congestive heart failure home monitoring pilot study in urban denver

Bakhshi, Saba; Li, Xin; Семенов, Н. В.; Apodaca-Madrid, Jesus; Mahoor, Mohammad H.; Newman, Keith; Long, Carlin S.; C, Neuman

doi:10.1109/iembs.2011.6090859

Cited by 9 publications

(18 citation statements)

References 8 publications

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“…Patient inclusion criteria for previous hospitalizations differed among the studies. If a hospital admission was required for inclusion, this was most often (n=10) within 7 days of discharge [ 4 , 23 , 24 , 29 , 35 , 37 , 39 - 42 ]. Inclusion was started within 1 month of index hospitalization for 2 studies [ 20 , 25 ], and 5 studies [ 21 , 32 , 43 - 45 ] included patients within 6 months.…”

Section: Resultsmentioning

confidence: 99%

The Effect of Noninvasive Telemonitoring for Chronic Heart Failure on Health Care Utilization: Systematic Review

Auener¹,

Remers²,

Dulmen³

et al. 2021

J Med Internet Res

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Background Chronic heart failure accounts for approximately 1%-2% of health care expenditures in most developed countries. These costs are primarily driven by hospitalizations and comorbidities. Telemonitoring has been proposed to reduce the number of hospitalizations and decrease the cost of treatment for patients with heart failure. However, the effects of telemonitoring on health care utilization remain unclear. Objective This systematic review aims to study the effect of telemonitoring programs on health care utilization and costs in patients with chronic heart failure. We assess the effect of telemonitoring on hospitalizations, emergency department visits, length of stay, hospital days, nonemergency department visits, and health care costs. Methods We searched PubMed, Embase, and Web of Science for randomized controlled trials and nonrandomized studies on noninvasive telemonitoring and health care utilization. We included studies published between January 2010 and August 2020. For each study, we extracted the reported data on the effect of telemonitoring on health care utilization. We used P<.05 and CIs not including 1.00 to determine whether the effect was statistically significant. Results We included 16 randomized controlled trials and 13 nonrandomized studies. Inclusion criteria, population characteristics, and outcome measures differed among the included studies. Most studies showed no effect of telemonitoring on health care utilization. The number of hospitalizations was significantly reduced in 38% (9/24) of studies, whereas emergency department visits were reduced in 13% (1/8) of studies. An increase in nonemergency department visits (6/9, 67% of studies) was reported. Health care costs showed ambiguous results, with 3 studies reporting an increase in health care costs, 3 studies reporting a reduction, and 4 studies reporting no significant differences. Health care cost reductions were realized through a reduction in hospitalizations, whereas increases were caused by the high costs of the telemonitoring program or increased health care utilization. Conclusions Most telemonitoring programs do not show clear effects on health care utilization measures, except for an increase in nonemergency outpatient department visits. This may be an unwarranted side effect rather than a prerequisite for effective telemonitoring. The consequences of telemonitoring on nonemergency outpatient visits should receive more attention from regulators, payers, and providers. This review further demonstrates the high clinical and methodological heterogeneity of telemonitoring programs. This should be taken into account in future meta-analyses aimed at identifying the effective components of telemonitoring programs.

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Section: Resultsmentioning

confidence: 99%

The Effect of Noninvasive Telemonitoring for Chronic Heart Failure on Health Care Utilization: Systematic Review

Auener¹,

Remers²,

Dulmen³

et al. 2021

J Med Internet Res

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“…[29] A number of home monitoring systems have been developed to support heart failure. For instance, a home monitoring system that only measured weight on a daily basis was found to reduce the length of patients' stay at the hospital [30]. Another system that incorporates artificial intelligence in the design was also reported [31].…”

Section: Background Literaturementioning

confidence: 99%

Designing Patient-Centered mHealth Technology Intervention to Reduce Hospital Readmission for Heart-Failure Patients

Alluhaidan

Lee

Alnosayan

et al. 2015

2015 48th Hawaii International Conference on System Sciences

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In the US, approximately 5.8 million people suffer from Congestive Heart Failure (CHF). It is the most frequent cause for hospital readmissions such that 21.2% of Medicare patients diagnosed with CHF were readmitted to hospital within 30 days of discharge. Tele-monitoring daily self-management regimens may reduce hospital readmission. MyHeart is a telehealth system, which we have designed for CHF patients to bridge the current gap in the Congestive Heart Failure care continuum when the patient moves to the home environment. The system uses wireless health devices and a mobile application to collect patients' data; moreover, it sends messages to patients to encourage self-care as per Fogg's behavior model. On the clinician's end, we designed a rule-based expert system and a dashboard to process and display patients' vitals, symptoms, and health risk. MyHeart is currently being used at Loma Linda University (LLU) Medical Center and we report here our initial findings.

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“…We focus in particular on numbers that end with certain digits (specifically, 0 or 5), since a tendency towards these digits has been reported across a wide range of areas [11–13]. End-digit preference is common and can reduce the predictive signal from patient-reported weight data – especially since, for patients with heart failure, clinically-significant changes in weight can be as small as two pounds (0.9 Kg) [14–18]. For example, if a patient regularly rounds their weight to the nearest five pounds, then a small change from 142 to 143 pounds from one day to the next would result in a false positive, since in that instance weight would be reported as jumping from 140 to 145 pounds.…”

Section: Introductionmentioning

confidence: 99%

Assessing the reliability of self-reported weight for the management of heart failure: application of fraud detection methods to a randomised trial of telemonitoring

Steventon

Chaudhry

Lin³

et al. 2017

BMC Med Inform Decis Mak

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BackgroundSince clinical management of heart failure relies on weights that are self-reported by the patient, errors in reporting will negatively impact the ability of health care professionals to offer timely and effective preventive care. Errors might often result from rounding, or more generally from individual preferences for numbers ending in certain digits, such as 0 or 5. We apply fraud detection methods to assess preferences for numbers ending in these digits in order to inform medical decision making.MethodsThe Telemonitoring to Improve Heart Failure Outcomes trial tested an approach to telemonitoring that used existing technology; intervention patients (n = 826) were asked to measure their weight daily using a digital scale and to relay measurements using their telephone keypads. First, we estimated the number of weights subject to end-digit preference by dividing the weights by five and comparing the resultant distribution with the uniform distribution. Then, we assessed the characteristics of patients reporting an excess number of weights ending in 0 or 5, adjusting for chance reporting of these values.ResultsOf the 114,867 weight readings reported during the trial, 18.6% were affected by end-digit preference, and the likelihood of these errors occurring increased with the number of days that had elapsed since trial enrolment (odds ratio per day: 1.002, p < 0.001). At least 105 patients demonstrated end-digit preference (14.9% of those who submitted data); although statistical significance was limited, a pattern emerged that, compared with other patients, they tended to be younger, male, high school graduates and on more medications. Patients with end-digit preference reported greater variability in weight, and they generated an average 2.9 alerts to the telemonitoring system over the six-month trial period (95% CI, 2.3 to 3.5), compared with 2.3 for other patients (95% CI, 2.2 to 2.5).ConclusionsAs well as overshadowing clinically meaningful changes in weight, end-digit preference can lead to false alerts to telemonitoring systems, which may be associated with unnecessary treatment and alert fatigue. In this trial, end-digit preference was common and became increasingly so over time. By applying fraud detection methods to electronic medical data, it is possible to produce clinically significant information that can inform the design of initiatives to improve the accuracy of reporting.Trial registrationClinicalTrials.gov registration number NCT00303212 March 2006.

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Congestive heart failure home monitoring pilot study in urban denver

Cited by 9 publications

References 8 publications

The Effect of Noninvasive Telemonitoring for Chronic Heart Failure on Health Care Utilization: Systematic Review

The Effect of Noninvasive Telemonitoring for Chronic Heart Failure on Health Care Utilization: Systematic Review

Designing Patient-Centered mHealth Technology Intervention to Reduce Hospital Readmission for Heart-Failure Patients

Assessing the reliability of self-reported weight for the management of heart failure: application of fraud detection methods to a randomised trial of telemonitoring

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