Effect of telehealth on hospital utilisation and mortality in routine clinical practice: A matched control cohort study in an early adopter site

BMJ Open

Volumn 6, Issue 2, 2016, Pages

  Steventon, Adam ^a   Ariti, Cono ^a   Fisher, Elizabeth ^b   Bardsley, Martin ^b  

^a Data Analytics   (United Kingdom)

^b Health Education England   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AGED; ARTICLE; CHRONIC OBSTRUCTIVE LUNG DISEASE; CLINICAL PRACTICE; CONGESTIVE HEART FAILURE; CONTROLLED STUDY; DIABETES MELLITUS; FEMALE; GENERAL PRACTICE; HEALTH CARE UTILIZATION; HOSPITAL ADMISSION; HOSPITAL CARE; HUMAN; INTERVENTION STUDY; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MEDICAL RECORD; MORTALITY; OBSERVATIONAL STUDY; PATIENT CODING; PREDICTIVE VALUE; RISK ASSESSMENT; SECONDARY HEALTH CARE; TELEHEALTH; UNITED KINGDOM; ENGLAND; EPIDEMIOLOGY; HEART FAILURE; HOSPITAL EMERGENCY SERVICE; HOSPITALIZATION; ORGANIZATION AND MANAGEMENT; OUTPATIENT DEPARTMENT; PULMONARY DISEASE, CHRONIC OBSTRUCTIVE; RETROSPECTIVE STUDY; RURAL POPULATION; STATISTICS AND NUMERICAL DATA; TELEMEDICINE; UTILIZATION;

AGED; CONFOUNDING FACTORS (EPIDEMIOLOGY); DIABETES MELLITUS; EMERGENCY SERVICE, HOSPITAL; ENGLAND; FEMALE; HEART FAILURE; HOSPITALIZATION; HUMANS; MALE; OUTPATIENT CLINICS, HOSPITAL; PULMONARY DISEASE, CHRONIC OBSTRUCTIVE; RETROSPECTIVE STUDIES; RURAL POPULATION; TELEMEDICINE;

EID: 84960372907     PISSN: None     EISSN: 20446055     Source Type: Journal    
DOI: 10.1136/bmjopen-2015-009221     Document Type: Article

References (44)

1. McLean S, Protti D, Sheikh A. Telehealthcare for long term conditions. BMJ 2011;342:d120.
2. Department of Health. A concordat between the Department of Health and the telehealth and telecare industry. London: Department of Health, 2012:1-4.
3. Chaudhry SI, Phillips CO, Stewart SS, et al. Telemonitoring for patients with chronic heart failure: a systematic review. J Card Fail 2007;13:56-62.
4. Steventon A, Bardsley M, Billings J, et al. Effect of telehealth on use of secondary care and mortality: findings from the Whole System Demonstrator cluster randomised trial. BMJ 2012;344:e3874.
5. Pinnock H, Hanley J, McCloughan L, et al. Effectiveness of telemonitoring integrated into existing clinical services on hospital admission for exacerbation of chronic obstructive pulmonary disease: researcher blind, multicentre, randomised controlled trial. BMJ 2013;347:f6070.
6. Chaudhry SI, Mattera JA, Curtis JP, et al. Telemonitoring in patients with heart failure. N Engl J Med 2010;363:2301-9.
7. Foster A, Horspool KA, Edwards L, et al. Who does not participate in telehealth trials and why? A cross-sectional survey. Trials 2015;16:258.
8. Steventon A, Grieve R, Bardsley M. An approach to assess generalizability in comparative effectiveness research: a case study of the Whole Systems Demonstrator cluster randomized trial comparing telehealth with usual care for patients with chronic health conditions. Med Decis Making 2015;35:1023-36.
9. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70:41-55 (cited 21 Aug. 2012).
10. Chumbler NR, Kobb R, Brennan DM, et al. Recommendations for research design of telehealth studies. Telemed e-Health 2008;14:986-9.
11. Baker LC, Johnson SJ, Macaulay D, et al. Integrated telehealth and care management program for Medicare beneficiaries with chronic disease linked to savings. Health Aff 2011;30:1689-97.
12. Chumbler NR, Vogel WB, Garel M, et al. Health services utilization of a care coordination/home-telehealth program for veterans with diabetes: a matched-cohort study. J Ambul Care Manage 2005;28:230-40.
13. Darkins A, Kendall S, Edmonson E, et al. Reduced cost and mortality using home telehealth to promote self-management of complex chronic conditions: a retrospective matched cohort study of 4, 999 veteran patients. Telemed J E Health 2015;21:70-6.
14. Rosenbaum PR. Observational studies. 2nd edn. New York: Springer, 2002:1-377 (cited 14 May 2014). http://www.amazon.com/Observational-Studies-Springer-Series-Statistics/dp/1441931910
15. Steventon A, Grieve R, Sekhon JS. A comparison of alternative strategies for choosing control populations in observational studies. Heal Serv Outcomes Res Methodol 2015;15:157-81.
16. Bower P, Cartwright M, Hirani SP, et al. A comprehensive evaluation of the impact of telemonitoring in patients with long-term conditions and social care needs: protocol for the whole systems demonstrator cluster randomised trial. BMC Health Serv Res 2011;11:184.
17. Purdy S, Griffin T, Salisbury C, et al. Ambulatory care sensitive conditions: terminology and disease coding need to be more specific to aid policy makers and clinicians. Public Health 2009;123:169-73.
18. Agency for Healthcare Quality and Research. AHRQ quality indicators -guide to prevention quality indicators: hospital admission for ambulatory care sensitive conditions. Rockville, MD: Agency for Healthcare Quality and Research, 2004.
19. Health and Social Care Information Centre. Hospital Episodes Statistics. www.hscic.gov. uk/hes (accessed 17 Jan 2016).
20. Billings J, Dixon J, Mijanovich T, et al. Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients. BMJ 2006;333:327.
21. Communities and Local Government. The English indices of deprivation 2010. London: Communities and Local Government 2011.
22. Hansen BB. The prognostic analogue of the propensity score. Biometrika 2008;95:481-8 (cited 1 Aug. 2012).
23. Austin PC. A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Stat Med 2008;27:2037-49 (cited 21 Aug. 2021).
24. Imai K, King G, Stuart EA. Misunderstandings between experimentalists and observationalists about causal inference. J R Stat Soc Ser A 2008;171:481-502.
25. Normand ST, Landrum MB, Guadagnoli E, et al. Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores. J Clin Epidemiol 2001;54:387-98.
26. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med 2009;28:3083-107.
27. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc 1958;53:457-81.
28. Cox D. Regression models and life-tables. J R Stat Soc Ser B 1972;34:187-220.
29. Austin PC. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Stat Med 2014;33:1242-58.
30. Shadish WR, Cook TD, Campbell DT. Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin, 2002.
31. Stuart EA, Huskamp HA, Duckworth K, et al. Using propensity scores in difference-in-differences models to estimate the effects of a policy change. Heal Serv Outcomes Res Methodol 2014;14:166-82 (cited 21 Dec. 2014).
32. Groenwold RH, Hak E, Hoes AW. Quantitative assessment of unobserved confounding is mandatory in nonrandomized intervention studies. J Clin Epidemiol 2009;62:22-8.
33. Carnegie NB, Harada M, Hill J. treatSens: A Package to Assess Sensitivity of Causal Analyses to Unobserved Confounding. R package version 2.0. New York, NY: 2015. https://cran. r-project.org/web/packages/treatSens/index.html (accessed 17 Jan 2016).
34. Spencer SA, Davies MP. Hospital episode statistics: improving the quality and value of hospital data: a national internet e-survey of hospital consultants. BMJ Open 2012;2: pii: e001651 (cited 11 Jun. 2013).
35. Deeny SR, Steventon A. Making sense of the shadows: Priorities for creating a learning health care system based on routinely collected data. BMJ Qual Saf 2015;24:505-15.
36. 1c and likelihood of hospital admission in people with type 1 diabetes in the Scottish population: a study from the Scottish Diabetes Research Network Epidemiology Group. Diabetes Care 2011;34:1992-7.
37. Craig P, Dieppe P, MacIntyre S, et al. Developing and evaluating complex interventions: new guidance. London: Medical Research Council, 2008.
38. Hendy J, Chrysanthaki T, Barlow J, et al. An organisational analysis of the implementation of telecare and telehealth: the whole systems demonstrator. BMC Health Serv Res 2012;12:403 (cited 19 November 2012).
39. McCarney R, Warner J, Iliffe S, et al. The Hawthorne Effect: a randomised, controlled trial. BMC Med Res Methodol 2007;7:30 (cited 4 Nov. 2012).
40. Shea S, Weinstock RS, Teresi JA, et al. A randomized trial comparing telemedicine case management with usual care in older, ethnically diverse, medically underserved patients with diabetes mellitus: 5 year results of the IDEATel study. J Am Med Informatics Assoc 2009;16:446-56.
41. Darkins A, Ryan P, Kobb R. coordination/home telehealth: the systematic implementation of health informatics, home telehealth, and disease management to support the care of veteran patients. Telemed J E Health 2008;2000:3-5.
42. Chaudhry SI, Wang Y, Concato J, et al. Patterns of weight change preceding hospitalization for heart failure. Circulation 2007;116:1549-54 (cited 18 Jun. 2013).
43. Weiner JP. The Johns Hopkins ACG System: technical reference guide version 10.0. Baltimore, MD: Johns Hopkins University, 2011.
44. Sekhon JS, Grieve RD. A matching method for improving covariate balance in cost-effectiveness analyses. Health Econ 2012;21:695-714.