TY - JOUR
T1 - The effect of telehealth on hospital services use
T2 - Systematic review and meta-analysis
AU - Peters, Guido M.
AU - Kooij, Laura
AU - Lenferink, Anke
AU - Van Harten, Wim H.
AU - Doggen, Carine J.M.
N1 - Funding Information:
GMP, LK, AL, and CJMD have nothing to declare. WHVH received nonrestricted research grants from Novartis and Agendia BV.
Funding Information:
This study was funded by a nonrestricted grant from Menzis, a Dutch insurance company. The funding source was not involved in the design of the study, data analysis, writing of the manuscript, or the decision to submit for publication.
Publisher Copyright:
© 2021 Journal of Medical Internet Research. All rights reserved.
Financial transaction number:
342128529
PY - 2021/9/1
Y1 - 2021/9/1
N2 - Background: Telehealth interventions, that is, health care provided over a distance using information and communication technology, are suggested as a solution to rising health care costs by reducing hospital service use. However, the extent to which this is possible is unclear. Objective: The aim of this study is to evaluate the effect of telehealth on the use of hospital services, that is, (duration of) hospitalizations, and to compare the effects between telehealth types and health conditions. Methods: We searched PubMed, Scopus, and the Cochrane Library from inception until April 2019. Peer-reviewed randomized controlled trials (RCTs) reporting the effect of telehealth interventions on hospital service use compared with usual care were included. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool and quality of evidence according to the Grading of Recommendations Assessment, Development and Evaluation guidelines. Results: We included 127 RCTs in the meta-analysis. Of these RCTs, 82.7% (105/127) had a low risk of bias or some concerns overall. High-quality evidence shows that telehealth reduces the risk of all-cause or condition-related hospitalization by 18 (95% CI 0-30) and 37 (95% CI 20-60) per 1000 patients, respectively. We found high-quality evidence that telehealth leads to reductions in the mean all-cause and condition-related hospitalizations, with 50 and 110 fewer hospitalizations per 1000 patients, respectively. Overall, the all-cause hospital days decreased by 1.07 (95% CI -1.76 to -0.39) days per patient. For hospitalized patients, the mean hospital stay for condition-related hospitalizations decreased by 0.89 (95% CI -1.42 to -0.36) days. The effects were similar between telehealth types and health conditions. A trend was observed for studies with longer follow-up periods yielding larger effects. Conclusions: Small to moderate reductions in hospital service use can be achieved using telehealth. It should be noted that, despite the large number of included studies, uncertainties around the magnitude of effects remain, and not all effects are statistically significant.
AB - Background: Telehealth interventions, that is, health care provided over a distance using information and communication technology, are suggested as a solution to rising health care costs by reducing hospital service use. However, the extent to which this is possible is unclear. Objective: The aim of this study is to evaluate the effect of telehealth on the use of hospital services, that is, (duration of) hospitalizations, and to compare the effects between telehealth types and health conditions. Methods: We searched PubMed, Scopus, and the Cochrane Library from inception until April 2019. Peer-reviewed randomized controlled trials (RCTs) reporting the effect of telehealth interventions on hospital service use compared with usual care were included. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool and quality of evidence according to the Grading of Recommendations Assessment, Development and Evaluation guidelines. Results: We included 127 RCTs in the meta-analysis. Of these RCTs, 82.7% (105/127) had a low risk of bias or some concerns overall. High-quality evidence shows that telehealth reduces the risk of all-cause or condition-related hospitalization by 18 (95% CI 0-30) and 37 (95% CI 20-60) per 1000 patients, respectively. We found high-quality evidence that telehealth leads to reductions in the mean all-cause and condition-related hospitalizations, with 50 and 110 fewer hospitalizations per 1000 patients, respectively. Overall, the all-cause hospital days decreased by 1.07 (95% CI -1.76 to -0.39) days per patient. For hospitalized patients, the mean hospital stay for condition-related hospitalizations decreased by 0.89 (95% CI -1.42 to -0.36) days. The effects were similar between telehealth types and health conditions. A trend was observed for studies with longer follow-up periods yielding larger effects. Conclusions: Small to moderate reductions in hospital service use can be achieved using telehealth. It should be noted that, despite the large number of included studies, uncertainties around the magnitude of effects remain, and not all effects are statistically significant.
KW - EHealth
KW - Health services use
KW - Hospitalization
KW - Meta-analysis
KW - Systematic review
KW - Telehealth
UR - http://www.scopus.com/inward/record.url?scp=85114337519&partnerID=8YFLogxK
U2 - 10.2196/25195
DO - 10.2196/25195
M3 - Review article
C2 - 34468324
AN - SCOPUS:85114337519
SN - 1439-4456
VL - 23
JO - Journal of medical internet research
JF - Journal of medical internet research
IS - 9
M1 - e25195
ER -