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Konnyu KJ, Yogasingam S, Lépine J, Sullivan K, Alabousi M, Edwards A, Hillmer M, Karunananthan S, Lavis JN, Linklater S, Manns BJ, Moher D, Mortazhejri S, Nazarali S, Paprica PA, Ramsay T, Ryan PM, Sargious P, Shojania KG, Straus SE, Tonelli M, Tricco A, Vachon B, Yu CH, Zahradnik M, Trikalinos TA, Grimshaw JM, Ivers N. Quality improvement strategies for diabetes care: Effects on outcomes for adults living with diabetes. Cochrane Database Syst Rev 2023; 5:CD014513. [PMID: 37254718 PMCID: PMC10233616 DOI: 10.1002/14651858.cd014513] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
BACKGROUND There is a large body of evidence evaluating quality improvement (QI) programmes to improve care for adults living with diabetes. These programmes are often comprised of multiple QI strategies, which may be implemented in various combinations. Decision-makers planning to implement or evaluate a new QI programme, or both, need reliable evidence on the relative effectiveness of different QI strategies (individually and in combination) for different patient populations. OBJECTIVES To update existing systematic reviews of diabetes QI programmes and apply novel meta-analytical techniques to estimate the effectiveness of QI strategies (individually and in combination) on diabetes quality of care. SEARCH METHODS We searched databases (CENTRAL, MEDLINE, Embase and CINAHL) and trials registers (ClinicalTrials.gov and WHO ICTRP) to 4 June 2019. We conducted a top-up search to 23 September 2021; we screened these search results and 42 studies meeting our eligibility criteria are available in the awaiting classification section. SELECTION CRITERIA We included randomised trials that assessed a QI programme to improve care in outpatient settings for people living with diabetes. QI programmes needed to evaluate at least one system- or provider-targeted QI strategy alone or in combination with a patient-targeted strategy. - System-targeted: case management (CM); team changes (TC); electronic patient registry (EPR); facilitated relay of clinical information (FR); continuous quality improvement (CQI). - Provider-targeted: audit and feedback (AF); clinician education (CE); clinician reminders (CR); financial incentives (FI). - Patient-targeted: patient education (PE); promotion of self-management (PSM); patient reminders (PR). Patient-targeted QI strategies needed to occur with a minimum of one provider or system-targeted strategy. DATA COLLECTION AND ANALYSIS We dual-screened search results and abstracted data on study design, study population and QI strategies. We assessed the impact of the programmes on 13 measures of diabetes care, including: glycaemic control (e.g. mean glycated haemoglobin (HbA1c)); cardiovascular risk factor management (e.g. mean systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), proportion of people living with diabetes that quit smoking or receiving cardiovascular medications); and screening/prevention of microvascular complications (e.g. proportion of patients receiving retinopathy or foot screening); and harms (e.g. proportion of patients experiencing adverse hypoglycaemia or hyperglycaemia). We modelled the association of each QI strategy with outcomes using a series of hierarchical multivariable meta-regression models in a Bayesian framework. The previous version of this review identified that different strategies were more or less effective depending on baseline levels of outcomes. To explore this further, we extended the main additive model for continuous outcomes (HbA1c, SBP and LDL-C) to include an interaction term between each strategy and average baseline risk for each study (baseline thresholds were based on a data-driven approach; we used the median of all baseline values reported in the trials). Based on model diagnostics, the baseline interaction models for HbA1c, SBP and LDL-C performed better than the main model and are therefore presented as the primary analyses for these outcomes. Based on the model results, we qualitatively ordered each QI strategy within three tiers (Top, Middle, Bottom) based on its magnitude of effect relative to the other QI strategies, where 'Top' indicates that the QI strategy was likely one of the most effective strategies for that specific outcome. Secondary analyses explored the sensitivity of results to choices in model specification and priors. Additional information about the methods and results of the review are available as Appendices in an online repository. This review will be maintained as a living systematic review; we will update our syntheses as more data become available. MAIN RESULTS We identified 553 trials (428 patient-randomised and 125 cluster-randomised trials), including a total of 412,161 participants. Of the included studies, 66% involved people living with type 2 diabetes only. Participants were 50% female and the median age of participants was 58.4 years. The mean duration of follow-up was 12.5 months. HbA1c was the commonest reported outcome; screening outcomes and outcomes related to cardiovascular medications, smoking and harms were reported infrequently. The most frequently evaluated QI strategies across all study arms were PE, PSM and CM, while the least frequently evaluated QI strategies included AF, FI and CQI. Our confidence in the evidence is limited due to a lack of information on how studies were conducted. Four QI strategies (CM, TC, PE, PSM) were consistently identified as 'Top' across the majority of outcomes. All QI strategies were ranked as 'Top' for at least one key outcome. The majority of effects of individual QI strategies were modest, but when used in combination could result in meaningful population-level improvements across the majority of outcomes. The median number of QI strategies in multicomponent QI programmes was three. Combinations of the three most effective QI strategies were estimated to lead to the below effects: - PR + PSM + CE: decrease in HbA1c by 0.41% (credibility interval (CrI) -0.61 to -0.22) when baseline HbA1c < 8.3%; - CM + PE + EPR: decrease in HbA1c by 0.62% (CrI -0.84 to -0.39) when baseline HbA1c > 8.3%; - PE + TC + PSM: reduction in SBP by 2.14 mmHg (CrI -3.80 to -0.52) when baseline SBP < 136 mmHg; - CM + TC + PSM: reduction in SBP by 4.39 mmHg (CrI -6.20 to -2.56) when baseline SBP > 136 mmHg; - TC + PE + CM: LDL-C lowering of 5.73 mg/dL (CrI -7.93 to -3.61) when baseline LDL < 107 mg/dL; - TC + CM + CR: LDL-C lowering by 5.52 mg/dL (CrI -9.24 to -1.89) when baseline LDL > 107 mg/dL. Assuming a baseline screening rate of 50%, the three most effective QI strategies were estimated to lead to an absolute improvement of 33% in retinopathy screening (PE + PR + TC) and 38% absolute increase in foot screening (PE + TC + Other). AUTHORS' CONCLUSIONS There is a significant body of evidence about QI programmes to improve the management of diabetes. Multicomponent QI programmes for diabetes care (comprised of effective QI strategies) may achieve meaningful population-level improvements across the majority of outcomes. For health system decision-makers, the evidence summarised in this review can be used to identify strategies to include in QI programmes. For researchers, this synthesis identifies higher-priority QI strategies to examine in further research regarding how to optimise their evaluation and effects. We will maintain this as a living systematic review.
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Affiliation(s)
- Kristin J Konnyu
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Sharlini Yogasingam
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Johanie Lépine
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Katrina Sullivan
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | | | - Alun Edwards
- Department of Medicine, University of Calgary, Calgary, Canada
| | - Michael Hillmer
- Institute for Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
| | - Sathya Karunananthan
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Interdisciplinary School of Health Sciences, University of Ottawa, Ottawa, Canada
| | - John N Lavis
- McMaster Health Forum, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada
| | - Stefanie Linklater
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Braden J Manns
- Department of Medicine and Community Health Sciences, University of Calgary, Calgary, Canada
| | - David Moher
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Sameh Mortazhejri
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Samir Nazarali
- Department of Ophthalmology and Visual Sciences, University of Alberta, Edmonton, Canada
| | - P Alison Paprica
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
| | - Timothy Ramsay
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | | | - Peter Sargious
- Department of Medicine, University of Calgary, Calgary, Canada
| | - Kaveh G Shojania
- University of Toronto Centre for Patient Safety, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Sharon E Straus
- Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital and University of Toronto, Toronto, Canada
| | - Marcello Tonelli
- Department of Medicine and Community Health Sciences, University of Calgary, Calgary, Canada
| | - Andrea Tricco
- Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital and University of Toronto, Toronto, Canada
- Epidemiology Division and Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Queen's Collaboration for Health Care Quality: A JBI Centre of Excellence, Queen's University, Kingston, Canada
| | - Brigitte Vachon
- School of Rehabilitation, Occupational Therapy Program, University of Montreal, Montreal, Canada
| | - Catherine Hy Yu
- Department of Medicine, St. Michael's Hospital, Toronto, Canada
| | - Michael Zahradnik
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Thomas A Trikalinos
- Departments of Health Services, Policy, and Practice and Biostatistics, Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Jeremy M Grimshaw
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Medicine, University of Ottawa, Ottawa, Canada
| | - Noah Ivers
- Department of Family and Community Medicine, Women's College Hospital, Toronto, Canada
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Lewinski AA, Walsh C, Rushton S, Soliman D, Carlson SM, Luedke MW, Halpern DJ, Crowley MJ, Shaw RJ, Sharpe JA, Alexopoulos AS, Tabriz AA, Dietch JR, Uthappa DM, Hwang S, Ball Ricks KA, Cantrell S, Kosinski AS, Ear B, Gordon AM, Gierisch JM, Williams JW, Goldstein KM. Telehealth for the Longitudinal Management of Chronic Conditions: Systematic Review. J Med Internet Res 2022; 24:e37100. [PMID: 36018711 PMCID: PMC9463619 DOI: 10.2196/37100] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 07/05/2022] [Accepted: 07/08/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Extensive literature support telehealth as a supplement or adjunct to in-person care for the management of chronic conditions such as congestive heart failure (CHF) and type 2 diabetes mellitus (T2DM). Evidence is needed to support the use of telehealth as an equivalent and equitable replacement for in-person care and to assess potential adverse effects. OBJECTIVE We conducted a systematic review to address the following question: among adults, what is the effect of synchronous telehealth (real-time response among individuals via phone or phone and video) compared with in-person care (or compared with phone, if synchronous video care) for chronic management of CHF, chronic obstructive pulmonary disease, and T2DM on key disease-specific clinical outcomes and health care use? METHODS We followed systematic review methodologies and searched two databases (MEDLINE and Embase). We included randomized or quasi-experimental studies that evaluated the effect of synchronously delivered telehealth for relevant chronic conditions that occurred over ≥2 encounters and in which some or all in-person care was supplanted by care delivered via phone or video. We assessed the bias using the Cochrane Effective Practice and Organization of Care risk of bias (ROB) tool and the certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation. We described the findings narratively and did not conduct meta-analysis owing to the small number of studies and the conceptual heterogeneity of the identified interventions. RESULTS We identified 8662 studies, and 129 (1.49%) were reviewed at the full-text stage. In total, 3.9% (5/129) of the articles were retained for data extraction, all of which (5/5, 100%) were randomized controlled trials. The CHF study (1/5, 20%) was found to have high ROB and randomized patients (n=210) to receive quarterly automated asynchronous web-based review and follow-up of telemetry data versus synchronous personal follow-up (in-person vs phone-based) for 1 year. A 3-way comparison across study arms found no significant differences in clinical outcomes. Overall, 80% (4/5) of the studies (n=466) evaluated synchronous care for patients with T2DM (ROB was judged to be low for 2, 50% of studies and high for 2, 50% of studies). In total, 20% (1/5) of the studies were adequately powered to assess the difference in glycosylated hemoglobin level between groups; however, no significant difference was found. Intervention design varied greatly from remote monitoring of blood glucose combined with video versus in-person visits to an endocrinology clinic to a brief, 3-week remote intervention to stabilize uncontrolled diabetes. No articles were identified for chronic obstructive pulmonary disease. CONCLUSIONS This review found few studies with a variety of designs and interventions that used telehealth as a replacement for in-person care. Future research should consider including observational studies and studies on additional highly prevalent chronic diseases.
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Affiliation(s)
- Allison A Lewinski
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States
- School of Nursing, Duke University, Durham, NC, United States
| | - Conor Walsh
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States
- Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Sharron Rushton
- School of Nursing, Duke University, Durham, NC, United States
| | - Diana Soliman
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Scott M Carlson
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Matthew W Luedke
- Department of Neurology, Duke University Medical Center, Durham, NC, United States
- Neurodiagnostic Center, Durham Veterans Affairs Medical Center, Durham, NC, United States
| | - David J Halpern
- Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
- Duke Primary Care, Duke University Medical Center, Durham, NC, United States
| | - Matthew J Crowley
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Ryan J Shaw
- School of Nursing, Duke University, Durham, NC, United States
| | - Jason A Sharpe
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States
| | - Anastasia-Stefania Alexopoulos
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Amir Alishahi Tabriz
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, United States
| | - Jessica R Dietch
- School of Psychological Science, Oregon State University, Corvallis, OR, United States
| | - Diya M Uthappa
- Doctor of Medicine Program, Duke University School of Medicine, Durham, NC, United States
| | - Soohyun Hwang
- Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States
| | - Katharine A Ball Ricks
- Cecil G Sheps Center for Health Service Research, University of North Carolina, Chapel Hill, NC, United States
| | - Sarah Cantrell
- Duke University Medical Center Library, Duke University School of Medicine, Durham, NC, United States
| | - Andrzej S Kosinski
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States
| | - Belinda Ear
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States
| | - Adelaide M Gordon
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States
| | - Jennifer M Gierisch
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States
- Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, United States
| | - John W Williams
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States
- Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Karen M Goldstein
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States
- Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
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Beks H, King O, Clapham R, Alston L, Glenister K, McKinstry C, Quilliam C, Wellwood I, Williams C, Wong Shee A. Community health programs delivered through information and communications technology in high-income countries: a scoping review (Preprint). J Med Internet Res 2020; 24:e26515. [PMID: 35262498 PMCID: PMC8943572 DOI: 10.2196/26515] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 04/15/2021] [Accepted: 11/18/2021] [Indexed: 11/13/2022] Open
Abstract
Background The COVID-19 pandemic has required widespread and rapid adoption of information and communications technology (ICT) platforms by health professionals. Transitioning health programs from face-to-face to remote delivery using ICT platforms has introduced new challenges. Objective The objective of this review is to scope for ICT-delivered health programs implemented within the community health setting in high-income countries and rapidly disseminate findings to health professionals. Methods The Joanna Briggs Institute’s scoping review methodology guided the review of the literature. Results The search retrieved 7110 unique citations. Each title and abstract was screened by at least two reviewers, resulting in 399 citations for full-text review. Of these 399 citations, 72 (18%) were included. An additional 27 citations were identified through reviewing the reference lists of the included studies, resulting in 99 citations. Citations examined 83 ICT-delivered programs from 19 high-income countries. Variations in program design, ICT platforms, research design, and outcomes were evident. Conclusions Included programs and research were heterogeneous, addressing prevalent chronic diseases. Evidence was retrieved for the effectiveness of nurse and allied health ICT-delivered programs. Findings indicated that outcomes for participants receiving ICT-delivered programs, when compared with participants receiving in-person programs, were either equivalent or better. Gaps included a paucity of co-designed programs, qualitative research around group programs, programs for patients and carers, and evaluation of cost-effectiveness. During COVID-19 and beyond, health professionals in the community health setting are encouraged to build on existing knowledge and address evidence gaps by developing and evaluating innovative ICT-delivered programs in collaboration with consumers and carers.
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Affiliation(s)
- Hannah Beks
- School of Medicine, Deakin University, Geelong, Australia
| | | | - Renee Clapham
- St Vincents Health Australia, Melbourne, Australia
- Ballarat Health Services, Ballarat, Australia
| | - Laura Alston
- School of Medicine, Deakin University, Geelong, Australia
- Colac Area Health, Colac, Australia
- Global Obesity Centre, Institute for Health Transformation, Deakin University, Geelong, Australia
| | - Kristen Glenister
- Department of Rural Health, University of Melbourne, Wangaratta, Australia
- Department of Rural Health, University of Melbourne, Shepparton, Australia
| | - Carol McKinstry
- La Trobe Rural Health School, La Trobe University, Bendigo, Australia
| | - Claire Quilliam
- Department of Rural Health, University of Melbourne, Wangaratta, Australia
| | - Ian Wellwood
- Faculty of Health Sciences, Australian Catholic University, Ballarat, Australia
| | | | - Anna Wong Shee
- School of Medicine, Deakin University, Geelong, Australia
- Ballarat Health Services, Ballarat, Australia
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