Eight‐year post‐trial follow‐up of health care and long‐term care costs of tele‐based health coaching

Mustonen, Erja; Hörhammer, Iiris; Absetz, Pilvikki; Patja, Kristiina; Lammintakanen, Johanna; Talja, Martti; Kuronen, Risto; Linna, Miika

doi:10.1111/1475-6773.13251

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…Only one article did not report a result as compared to the control group because it was a posttrial analysis and it did not address the control group. [28,32,35,38,40,41,43,46,[48][49][50][53][54][55][58][59][60]64] Telemedicine improved results compared to control 9 ( 14) [27,30,33,34,39,45,51,56,62] No statistically significant difference 7 (11) [24,25,43,47,49,50,63] Improved patient satisfaction 6 (9) [36,49,60,62,64,67] Improved health behaviors 6 (9) [22,38,40,41,49,67] Improved compliance/adherence 5 (8) [28,…”

Section: Comparisons To a Control Groupmentioning

confidence: 99%

Facilitators and Barriers to the Adoption of Telemedicine During the First Year of COVID-19: Systematic Review

Kruse¹,

Heinemann²

2022

J Med Internet Res

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Background The virulent and unpredictable nature of COVID-19 combined with a change in reimbursement mechanisms both forced and enabled the rapid adoption of telemedicine around the world. Thus, it is important to now assess the effects of this rapid adoption and to determine whether the barriers to such adoption are the same today as they were under prepandemic conditions. Objective The objective of this systematic literature review was to examine the research literature published during the COVID-19 pandemic to identify facilitators, barriers, and associated medical outcomes as a result of adopting telemedicine, and to determine if changes have occurred in the industry during this time. Methods The systematic review was performed in accordance with the Kruse protocol and the results are reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We analyzed 46 research articles from five continents published during the first year of the COVID-19 pandemic that were retrieved from searches in four research databases: PubMed (MEDLINE), CINAHL, Science Direct, and Web of Science. Results Reviewers identified 25 facilitator themes and observations, 12 barrier themes and observations, and 14 results (compared to a control group) themes and observations. Overall, 22% of the articles analyzed reported strong satisfaction or satisfaction (zero reported a decline in satisfaction), 27% reported an improvement in administrative or efficiency results (as compared with a control group), 14% reported no statistically significant difference from the control group, and 40% and 10% reported an improvement or no statistically significant difference in medical outcomes using the telemedicine modality over the control group, respectively. Conclusions The pandemic encouraged rapid adoption of telemedicine, which also encouraged practices to adopt the modality regardless of the challenges identified in previous research. Several barriers remain for health policymakers to address; however, health care administrators can feel confident in the modality as the evidence largely shows that it is safe, effective, and widely accepted.

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Section: Comparisons To a Control Groupmentioning

confidence: 99%

Facilitators and Barriers to the Adoption of Telemedicine During the First Year of COVID-19: Systematic Review

Kruse¹,

Heinemann²

2022

J Med Internet Res

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“…It allowed us to make some observations of reduced cardiovascular events among those who participated to the study. In our previous TERVA -trial reporting [ 19 ], we found that the 8-year total social and health care costs were lower in intervention group; in ITT -analysis 3% lower (approx. €1200/patient/8-year) and in PP-analysis 14% lower (approx.…”

Section: Discussionmentioning

confidence: 79%

“…In long-term follow-up, over 8 years, we found that the total social and health care costs were lower in the intervention group. During the first two years costs were higher in THC -group, but after that the costs were lower to the end of the follow-up period [ 19 ]. While behavioural changes, such as health coaches’ learning of coaching skills and patients’ lifestyle changes, can take 1–3 years to actualize, the effectiveness of THC -intervention may be delayed [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Eight-year post-trial follow-up of morbidity and mortality of telephone health coaching

Mustonen

Hörhammer

Patja

et al. 2021

BMC Health Serv Res

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Background Health coaching is a patient-centred approach to supporting self-management for the chronic conditions. However, long-term evidence of effectiveness of health coaching remains scarce. The object of this study was to evaluate the long-term effect of telephone health coaching (THC) on mortality and morbidity among people with type 2 diabetes (T2D), coronary artery disease (CAD) and congestive heart failure (CHF).. Methods 1535 T2D, CAD and CHF patients with unmet treatment targets were randomly allocated into an intervention group (n = 1034) and control group (n = 501). Intervention group received monthly individual strength-based, autonomy supportive THC sessions (average 30 min) for behavior change with a specially trained nurse for 12 months additional to usual health care. Control group received usual health care services. The primary outcome was a composite of death from cardiovascular causes or non-fatal stroke or non-fatal myocardial infarction (AMI) or unstable angina pectoris (UAP) during a follow-up of 8 years Three other composite endpoints with distinct combinations of fatal and non-fatal cardiovascular events and death from any cause were used as secondary outcomes. Other outcomes followed were the most relevant components of the composite endpoints. Randomized controlled trial (RCT) data was linked to Finnish national health and social care registries and electronic health records (EHR). Post-trial eight-year evaluation was conducted using intention-to-treat (ITT) and per-protocol (PP) analysis. Results The composite primary outcome event rate per 100 person years was lower in the intervention group (3.45) than in control group (3.88) in ITT -analysis, but the difference was not statistically significant (hazard ratio in the intervention group 0.87; 95% CI, 0.71 to 1.07; P = 0.19). In the subgroup (T2D, CAD/CHF) analysis, there were no statistically significant effects. The secondary PP-analysis showed statistically significant benefits for those who participated in the study. Conclusions No statistically significant effect of health coaching on mortality and morbidity was found in intention to treat analysis. The per protocol results suggest, however, that the intervention may be effective among patients who are willing and able to participate in health coaching. More research is needed to identify patients most likely to benefit from low-intensity health coaching. Trial registration NCT00552903 (registration date: the 1st of November 2007, updated the 3rd of February 2009).

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“…The costs for each diagnosis were calculated using the computational standard cost [ 46 , 47 ] using patient grouping methods and standard unit costs calculated from national-level cost accounting projects. Hospitalizations and hospital outpatient visits were grouped using the Nordic Diagnosis-Related Groups grouper.…”

Section: Methodsmentioning

confidence: 99%

Clustering Diagnoses From 58 Million Patient Visits in Finland Between 2015 and 2018

Fränti¹,

Sieranoja²,

Wikström³

et al. 2022

JMIR Med Inform

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Background Multiple chronic diseases in patients are a major burden on the health service system. Currently, diseases are mostly treated separately without paying sufficient attention to their relationships, which results in the fragmentation of the care process. The better integration of services can lead to the more effective organization of the overall health care system. Objective This study aimed to analyze the connections between diseases based on their co-occurrences to support decision-makers in better organizing health care services. Methods We performed a cluster analysis of diagnoses by using data from the Finnish Health Care Registers for primary and specialized health care visits and inpatient care. The target population of this study comprised those 3.8 million individuals (3,835,531/5,487,308, 69.90% of the whole population) aged ≥18 years who used health care services from the years 2015 to 2018. They had a total of 58 million visits. Clustering was performed based on the co-occurrence of diagnoses. The more the same pair of diagnoses appeared in the records of the same patients, the more the diagnoses correlated with each other. On the basis of the co-occurrences, we calculated the relative risk of each pair of diagnoses and clustered the data by using a graph-based clustering algorithm called the M-algorithm—a variant of k-means. Results The results revealed multimorbidity clusters, of which some were expected (eg, one representing hypertensive and cardiovascular diseases). Other clusters were more unexpected, such as the cluster containing lower respiratory tract diseases and systemic connective tissue disorders. The annual cost of all clusters was €10.0 billion, and the costliest cluster was cardiovascular and metabolic problems, costing €2.3 billion. Conclusions The method and the achieved results provide new insights into identifying key multimorbidity groups, especially those resulting in burden and costs in health care services.

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Eight‐year post‐trial follow‐up of health care and long‐term care costs of tele‐based health coaching

Cited by 8 publications

References 23 publications

Facilitators and Barriers to the Adoption of Telemedicine During the First Year of COVID-19: Systematic Review

Facilitators and Barriers to the Adoption of Telemedicine During the First Year of COVID-19: Systematic Review

Eight-year post-trial follow-up of morbidity and mortality of telephone health coaching

Clustering Diagnoses From 58 Million Patient Visits in Finland Between 2015 and 2018

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