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# Effect of telephone health coaching (Birmingham OwnHealth) on hospital use and associated costs: cohort study with matched controls

BMJ 2013; 347 (Published 06 August 2013) Cite this as: BMJ 2013;347:f4585
1. Adam Steventon, senior research analyst1,
2. Sarah Tunkel, director2,
3. Ian Blunt, senior research analyst1,
4. Martin Bardsley, head of research1
1. 1The Nuffield Trust, London W1G 7LP, UK
2. 2Ernst & Young, London SE1 2AF, UK
1. Correspondence to: A Steventon adam.steventon{at}nuffieldtrust.org.uk
• Accepted 9 July 2013

## Abstract

Objectives To test the effect of a telephone health coaching service (Birmingham OwnHealth) on hospital use and associated costs.

Design Analysis of person level administrative data. Difference-in-difference analysis was done relative to matched controls.

Setting Community based intervention operating in a large English city with industry.

Participants 2698 patients recruited from local general practices before 2009 with heart failure, coronary heart disease, diabetes, or chronic obstructive pulmonary disease; and a history of inpatient or outpatient hospital use. These individuals were matched on a 1:1 basis to control patients from similar areas of England with respect to demographics, diagnoses of health conditions, previous hospital use, and a predictive risk score.

Intervention Telephone health coaching involved a personalised care plan and a series of outbound calls usually scheduled monthly. Median length of time enrolled on the service was 25.5 months. Control participants received usual healthcare in their areas, which did not include telephone health coaching.

Main outcome measures Number of emergency hospital admissions per head over 12 months after enrolment. Secondary metrics calculated over 12 months were: hospital bed days, elective hospital admissions, outpatient attendances, and secondary care costs.

Results In relation to diagnoses of health conditions and other baseline variables, matched controls and intervention patients were similar before the date of enrolment. After this point, emergency admissions increased more quickly among intervention participants than matched controls (difference 0.05 admissions per head, 95% confidence interval 0.00 to 0.09, P=0.046). Outpatient attendances also increased more quickly in the intervention group (difference 0.37 attendances per head, 0.16 to 0.58, P<0.001), as did secondary care costs (difference £175 per head, £22 to £328, P=0.025). Checks showed that we were unlikely to have missed reductions in emergency admissions because of unobserved differences between intervention and matched control groups.

Conclusions The Birmingham OwnHealth telephone health coaching intervention did not lead to the expected reductions in hospital admissions or secondary care costs over 12 months, and could have led to increases.

## Introduction

Facing rising costs, healthcare systems around the world are exploring innovative ways to improve efficiency. Particular attention has been placed on the use of technology to help manage long term health conditions,1 including one-to-one telephone health coaching. This involves a regular series of phone calls between patient and health professional. The calls aim to provide support and encouragement to the patient and promote healthy behaviours such as treatment control, healthy diet, physical activity and mobility, rehabilitation, and good mental health.2 The hope is that the patient will maintain their own health more independently, and that the professional and patient will be in a better position to identify problems before they become critical. In turn, admissions to hospital may be prevented.3 Avoidable hospital admissions are both undesirable for the patient and expensive for the payer.

Video abstract

In a systematic review of telephone health coaching for people with long term conditions, only nine of 34 studies investigated effects on health service use.2 Four studies showed effects in this area, but findings were hard to generalise because the studies looked at a range of different health conditions, had relatively small samples (the average sample was fewer than 360 people), and included interventions that were heterogeneous. Five of the nine interventions included telemonitoring of vital signs such as blood pressure alongside telephone coaching. Since the review, other larger studies have been conducted.

### Sensitivity analysis for unobserved confounding

Within 12 months of the intervention, 2.3% of patients in both the intervention (n=63) and matched control (n=63) groups died in hospital. Sensitivity analysis simulated a hypothetical unobserved confounding variable and showed that, for the apparent increase in emergency admissions to be reversed, such a variable would need to be strongly associated with both intervention status and outcome, with odds ratios greater than 2.8. By comparison, insulin treatment (which is one variable we did not observe) had an odds ratio of 1.6 with intervention status.9

## Discussion

### Statement of findings

Telephone health coaching aims to support patients in managing their long term health conditions. The hope is that, by promoting healthy behaviours and by providing a means to identify problems before they become critical, telephone health coaching can help prevent crises that lead to hospital admissions. We compared a large sample of people receiving telephone health coaching in England to a well balanced, retrospectively matched control group using person level data. Rather than see a reduction in hospital activity in the study group, we found that emergency admissions increased at a faster rate among intervention patients than matched controls, as did outpatient attendances and secondary care costs. Therefore, there was no evidence of reductions in hospital admissions, and no savings were detected from which to offset the cost of the intervention.

### Strengths and weaknesses

We were able to study a large number of intervention patients with a high rate of data linkage (87%). Imperfect linkage was mainly due to imperfect recording of individual identifiers on the service’s operational system, because most records that did not link had missing or incomplete personal linkage data. On the assumption that recording omissions happened at random, our sample was an unbiased sample from the population receiving the intervention. Although the analysis then focussed on patients with previous hospital use, this variable is where the scope for savings was highest.

The use of administrative data meant that data were available for a high proportion of patients, and avoided problems of under-reporting by patients about how many services were used.32 However, the quality of data was not directly under our control. Potential problems with administrative data included limited insight into the quality and appropriateness of care,33 and observational intensity bias if coding practices varied between geographic areas.34

We obtained data for more patients than what our sample size calculation suggested was needed (2698 v 2035). Therefore, although we originally envisaged that we would only be able to detect differences in emergency admission rates of 15% or higher, the 13.6% increase detected was statistically significant, and was unlikely to be the result of chance (P=0.046). A 13.6% increase in emergency admissions is substantial for the health service, and much more than the general increase in age standardised rates of admission of 2.5% a year.35

The main risk to validity in this observational study was that, although intervention and matched control groups were similar in terms of an established set of predictors of future hospital use, they could have differed in ways that we could not observe (that is, there may have been unobserved confounding). Typically, only a small proportion of eligible patients receive complex interventions out of the hospital.36 Birmingham OwnHealth was a relatively established service, and at least 80% of the local general practices had participating patients. Nevertheless, there are around 9000 patients in the area with uncontrolled diabetes,37 for example, while only around 3000 patients received the intervention in the time period chosen.

We sought to minimise unobserved confounding by careful selection of the pool of potential controls, matching on previous outcomes and difference-in-difference estimation. The eligibility criteria for the service included clinical variables such as HbA1c. These variables were not recorded in our dataset. However, we ensured that the prevalence of health conditions was similar between the two groups, as were variables that are correlated with clinical indicators, such as hospital use.38 Sensitivity analysis showed that, although the increase in emergency admissions could conceivably have been caused by unobserved confounding, it is unlikely that we missed a reduction. To have missed such a reduction, the amount of unobserved confounding would have had to be greater than is realistic for clinical variables. Further, it is reassuring that no differences were observed in in-hospital mortality between the two groups. For example, if disease control had been worse among intervention patients, more deaths might have been expected.

Observational study designs have some advantages over randomised controlled trials. This study looked at a population that was selected to participate in telephone health coaching in routine practice. By contrast, randomised controlled trials may have poor generalisability when the patients in the trial differ from those who would receive the intervention routinely. Such differences might occur because of the selection of healthcare settings or practitioners for a trial, the choice of eligibility criteria for a trial, certain individuals preferring not to participate, or study design.39

This study investigated effects on hospital use and associated costs for people enrolled between 2006 and 2008. The effect of the service might have changed over time, because the eligibility criteria were later broadened to include chronic kidney disease, stroke, and transient ischaemic attack from 2009; and hypertension and older patients at risk from 2010. Although not the focus of this study, Birmingham OwnHealth might have affected the use of primary care or health related quality of life. Because the median duration of enrolment was several years, effects could have been over longer time periods than those analysed in this study. Other work has found improvements in clinical metrics among participants with poorly controlled diabetes,9 as well as high levels of patient satisfaction.8

### Comparison with other studies

Previous studies of the effect of telephone health coaching on service use have generally been encouraging. Four of nine studies identified by a systematic review found evidence of an effect on health service use, although sample sizes were typically small.2 A more recent, large randomised controlled trial found reductions in hospital admissions and expenditures.4 Before the current study, the largest observational study of telephone health coaching included 874 Medicaid members, and found no effect.6 The current study supports their findings on a larger sample drawn from England (n=2698).

One possible explanation for the apparently contradictory nature of the findings from these studies might be subtle differences in the design of the interventions. Aspects of intervention design such as the frequency of telephone calls vary widely between studies,2 although the profile of telephone calls in the current study (usually monthly) was not out of line. The largest randomised controlled trial4 included decision making for preference sensitive conditions, while three of the four effective interventions identified by the systematic review involved telemonitoring of vital signs in addition to health coaching. Telemonitoring could be effective at reducing hospital admissions even when combined with automated motivational messages and symptom questions rather than health coaching.15 This study adds weight to the conclusions suggested by the Medicaid studies6 7 that health coaching is not effective at reducing hospital use by itself. Further, although potentially explained by unobserved confounding, we found evidence that the intervention in Birmingham increased emergency admissions. Previous evaluations of other complex interventions out of hospital have also found indications of increases.14 One possibility is that increases occur as a result of greater observation. Indeed, in other settings, more intense observation and greater use of diagnostic tests have been found to correlate with the number of medical interventions made.40

Discrepancies between the findings of different studies could also be due to study settings, with both the Medicaid studies and the current study relating to a publicly insured population. Targeting the intervention using the outputs from a predictive risk model may increase effectiveness, as could better integration with existing primary and secondary care services. Finally, the evaluation method could affect results. Confounding is possible in observational studies, although our study design attempted to limit this threat to validity as much as possible.

### Conclusions

We conclude that Birmingham OwnHealth did not lead to the anticipated levels of reductions in hospital admissions or associated costs. Based on a systematic review and subsequent studies, including the present study, standard telephone health coaching seems unlikely to lead to reductions in hospital use, without the addition of other elements such as telemonitoring, shared decision making for preference sensitive conditions, or predictive modelling. More care coordination might also be needed. Unless health coachers have established relationships with other clinical staff, new interventions could prove to be additions to existing patterns of service use, rather than create efficiencies.

The study serves as a warning that efficacy as demonstrated by randomised controlled trials might not imply effectiveness in routine practice.41 Because administrative datasets are regularly updated, the methods used in the present study may be useful to monitor new services to ensure that benefits are achieved.

#### What is already known on this topic

• Telephone health coaching provides support and encouragement to patients to manage long term health conditions

• It is hoped that hospital admissions will be prevented as a result, creating efficiency gains for healthcare systems

• However, the current evidence base is unclear; many studies have been small and interventions are heterogeneous

• This study adds weight to the existing view that health coaching by itself is not effective at reducing hospital use over 12 months

• Coaching could be coupled with other interventions such as shared decision making or telemonitoring, and the context in which interventions are delivered might also be crucial

• Efficacy of new services as demonstrated by randomised controlled trials might not imply effectiveness in routine practice

## Notes

Cite this as: BMJ 2013;347:f4585

## Footnotes

• We thank staff in Birmingham East and North Primary Care Trust who organised data for scheme participants; John Grayland for his ongoing support; NHS Information for health and social care for providing invaluable support and acting as a trusted third party for the linkage to national hospital data; and Sally Inglis, Susannah McClean, and Doug Altman for their comments on a previous version of this manuscript. The data analysis for this paper was generated using the SAS software, version 9.3 (SAS Institute). SAS and all other SAS Institute product or service names are registered trademarks or trademarks of SAS Institute.

• Contributors: AS, IB, and MB designed the study. In addition, AS led the analysis and prepared the draft manuscript, ST liaised with the scheme about access to participant data, and IB derived unit costs for HES data. All authors reviewed the manuscript. AS was the study guarantor.

• Funding: This study was funded by the Department of Health in England, which reviewed the study protocol as part of the application for funding and agreed to publication. The views expressed are those of the authors and not of the Department of Health and does not constitute any form of assurance, legal opinion or advice. The organisations at which the authors are based shall have no liability to any third party in respect to the contents of this article.

• Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: funding from the Department of Health for the submitted work; AS, IB, and MB have a range of current or pending research grants on related topics from funding bodies including the National Institute for Health Research, Technology Strategy Board, and NHS trusts; ST, as an Ernst & Young employee, has declared that Ernst & Young is a consulting firm which may at times undertake consultancy work relevant to the commissioning and provision of community based care.

• Ethical approval: The ethics and confidentiality committee of the National Information Governance Board confirmed that data linkage (as described in the methods) was possible without explicit patient consent. The National Research Ethics Service confirmed that ethical approval was not required for this work, because it involved retrospective analysis of non-identifiable data for the purposes of service evaluation.

• Data sharing: No additional data available.

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