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Radford SJ, Abdul-Aema B, Tench C, Leighton P, Coad J, Moran GW. Substantial cost savings of ultrasound-based management over magnetic resonance imaging-based management in an inflammatory bowel disease service. Scand J Gastroenterol 2024; 59:683-689. [PMID: 38501494 DOI: 10.1080/00365521.2024.2330588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/08/2024] [Indexed: 03/20/2024]
Abstract
BACKGROUND Imaging is used to monitor disease activity in small bowel Crohn's disease (CD). Magnetic Resonance Enterography is often employed as a first modality in the United Kingdom for assessment and monitoring; however, waiting times, cost, patient burden and limited access are significant. It is as yet uncertain if small bowel intestinal ultrasound (IUS) may be a quicker, more acceptable, and cheaper alternative for monitoring patients with CD. METHODS A clinical service evaluation of imaging pathways was undertaken at a single NHS site in England, United Kingdom. Data were collected about patients who were referred and underwent an imaging analysis for their IBD. Only patients who underwent a therapy change were included in the analysis. Data were collected from care episodes between 01 January 2021-30 March 2022. RESULTS A combined total of 193 patient care episodes were reviewed, 107 from the IUS pathway and 86 from the MRE pathway. Estimated costs per patient in the IUS pathway was £78.86, and £375.35 per patient in the MRE pathway. The MRE pathway had an average time from referral to treatment initiation of 91 days (SD= ±61) with patients in the IUS pathway waiting an average of 46 days (SD= ±17). CONCLUSIONS Findings from this work indicate that IUS is a potential cost-saving option when compared to MRE when used in the management of CD. This is in addition to the cost difference of the radiological modalities. A large, multicentre, prospective study is needed to validate these initial findings.
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Affiliation(s)
- Shellie J Radford
- Nottingham NIHR Biomedical Research Centre, Nottingham University Hospitals NHS trust and the University of Nottingham, United Kingdom of Great Britain and Northern Ireland
- University of Nottingham, Translational Medical Sciences, School of Medicine, Faculty of Medicine and Health Sciences, United Kingdom of Great Britain and Northern Ireland
| | - Buraq Abdul-Aema
- Nottingham NIHR Biomedical Research Centre, Nottingham University Hospitals NHS trust and the University of Nottingham, United Kingdom of Great Britain and Northern Ireland
| | - Chris Tench
- University of Nottingham, Translational Medical Sciences, School of Medicine, Faculty of Medicine and Health Sciences, United Kingdom of Great Britain and Northern Ireland
| | - Paul Leighton
- University of Nottingham, Translational Medical Sciences, School of Medicine, Faculty of Medicine and Health Sciences, United Kingdom of Great Britain and Northern Ireland
| | - Jane Coad
- University of Nottingham, School of Health Sciences, Faculty of Medicine and Health Sciences, United Kingdom of Great Britain and Northern Ireland
| | - Gordon W Moran
- Nottingham NIHR Biomedical Research Centre, Nottingham University Hospitals NHS trust and the University of Nottingham, United Kingdom of Great Britain and Northern Ireland
- University of Nottingham, Translational Medical Sciences, School of Medicine, Faculty of Medicine and Health Sciences, United Kingdom of Great Britain and Northern Ireland
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Kaufman BG, Hastings SN, Meyer C, Stechuchak KM, Choate A, Decosimo K, Sullivan C, Wang V, Allen KD, Van Houtven CH. The business case for hospital mobility programs in the veterans health care system: Results from multi-hospital implementation of the STRIDE program. Health Serv Res 2024. [PMID: 38632179 DOI: 10.1111/1475-6773.14307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
OBJECTIVE To conduct a business case analysis for Department of Veterans Affairs (VA) program STRIDE (ASsisTed EaRly MobIlization for hospitalizeD older VEterans), which was designed to address immobility for hospitalized older adults. DATA SOURCES AND STUDY SETTING This was a secondary analysis of primary data from a VA 8-hospital implementation trial conducted by the Function and Independence Quality Enhancement Research Initiative (QUERI). In partnership with VA operational partners, we estimated resources needed for program delivery in and out of the VA as well as national implementation facilitation in the VA. A scenario analysis using wage data from the Bureau of Labor Statistics informs implementation decisions outside the VA. STUDY DESIGN This budget impact analysis compared delivery and implementation costs for two implementation strategies (Replicating Effective Programs [REP]+CONNECT and REP-only). To simulate national budget scenarios for implementation, we estimated the number of eligible hospitalizations nationally and varied key parameters (e.g., enrollment rates) to evaluate the impact of uncertainty. DATA COLLECTION Personnel time and implementation outcomes were collected from hospitals (2017-2019). Hospital average daily census and wage data were estimated as of 2022 to improve relevance to future implementation. PRINCIPAL FINDINGS Average implementation costs were $9450 for REP+CONNECT and $5622 for REP-only; average program delivery costs were less than $30 per participant in both VA and non-VA hospital settings. Number of walks had the most impact on delivery costs and ranged from 1 to 5 walks per participant. In sensitivity analyses, cost increased to $35 per participant if a physical therapist assistant conducts the walks. Among study hospitals, mean enrollment rates were higher among the REP+CONNECT hospitals (12%) than the REP-only hospitals (4%) and VA implementation costs ranged from $66 to $100 per enrolled. CONCLUSIONS STRIDE is a low-cost intervention, and program participation has the biggest impact on the resources needed for delivering STRIDE. TRIAL REGISTRATION ClinicalsTrials.gov NCT03300336. Prospectively registered on 3 October 2017.
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Affiliation(s)
- Brystana G Kaufman
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Medical Center, Durham, North Carolina, USA
- Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Duke Margolis Institute for Health Policy, Duke University, Durham, North Carolina, USA
| | - S Nicole Hastings
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Medical Center, Durham, North Carolina, USA
- Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Medicine, Duke University, Durham, North Carolina, USA
| | - Cassie Meyer
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Medical Center, Durham, North Carolina, USA
| | - Karen M Stechuchak
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Medical Center, Durham, North Carolina, USA
| | - Ashley Choate
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Medical Center, Durham, North Carolina, USA
| | - Kasey Decosimo
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Medical Center, Durham, North Carolina, USA
| | - Caitlin Sullivan
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Medical Center, Durham, North Carolina, USA
| | - Virginia Wang
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Medical Center, Durham, North Carolina, USA
- Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Duke Margolis Institute for Health Policy, Duke University, Durham, North Carolina, USA
- Department of Medicine, Duke University, Durham, North Carolina, USA
| | - Kelli D Allen
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Medical Center, Durham, North Carolina, USA
- Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Courtney H Van Houtven
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Medical Center, Durham, North Carolina, USA
- Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Duke Margolis Institute for Health Policy, Duke University, Durham, North Carolina, USA
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Donovan LM, McDowell JA, Pannick AP, Pai J, Bais AF, Plumley R, Wai TH, Grunwald GK, Josey K, Sayre GG, Helfrich CD, Zeliadt SB, Hoerster KD, Ma J, Au DH. Protocol for a pragmatic trial testing a self-directed lifestyle program targeting weight loss among patients with obstructive sleep apnea (POWER Trial). Contemp Clin Trials 2023; 135:107378. [PMID: 37935303 DOI: 10.1016/j.cct.2023.107378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 10/23/2023] [Accepted: 11/03/2023] [Indexed: 11/09/2023]
Abstract
BACKGROUND Obesity comprises the single greatest reversible risk factor for obstructive sleep apnea (OSA). Despite the potential of lifestyle-based weight loss services to improve OSA severity and symptoms, these programs have limited reach. POWER is a pragmatic trial of a remote self-directed weight loss care among patients with OSA. METHODS POWER randomizes 696 patients with obesity (BMI 30-45 kg/m2) and recent diagnosis or re-confirmation of OSA 1:1 to either a self-directed weight loss intervention or usual care. POWER tests whether such an intervention improves co-primary outcomes of weight and sleep-related quality of life at 12 months. Secondary outcomes include sleep symptoms, global ratings of change, and cardiovascular risk scores. Finally, consistent with a hybrid type 1 approach, the trial embeds an implementation process evaluation. We will use quantitative and qualitative methods including budget impact analyses and qualitative interviews to assess barriers to implementation. CONCLUSIONS The results of POWER will inform population health approaches to the delivery of weight loss care. A remote self-directed program has the potential to be disseminated widely with limited health system resources and likely low-cost.
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Affiliation(s)
- Lucas M Donovan
- Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA; University of Washington, Seattle, WA, USA.
| | - Jennifer A McDowell
- Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA
| | - Anna P Pannick
- Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA
| | - James Pai
- Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA; Tulane University, New Orleans, LA, USA
| | - Anthony F Bais
- Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA
| | - Robert Plumley
- Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA
| | | | | | | | - George G Sayre
- Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA
| | - Christian D Helfrich
- Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA; University of Washington, Seattle, WA, USA
| | - Steven B Zeliadt
- Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA; University of Washington, Seattle, WA, USA
| | - Katherine D Hoerster
- Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA; University of Washington, Seattle, WA, USA
| | - Jun Ma
- University of Illinois Chicago, Chicago, IL, USA
| | - David H Au
- Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA; University of Washington, Seattle, WA, USA
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Eisman AB, Whitman J, Palinkas LA, Fridline J, Harvey C, Kilbourne AM, Hutton DW. A mixed methods partner-focused cost and budget impact analysis to deploy implementation strategies for school-based prevention. Implement Sci Commun 2023; 4:133. [PMID: 37946235 PMCID: PMC10636820 DOI: 10.1186/s43058-023-00511-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 10/09/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Obtaining information on implementation strategy costs and local budget impacts from multiple perspectives is essential to data-driven decision-making about resource allocation for successful evidence-based intervention delivery. This mixed methods study determines the costs and priorities of deploying Enhanced Replicating Effective Programs (REP) to implement the Michigan Model for Health™, a universal school-based prevention intervention, from key shareholder perspectives. METHODS Our study included teachers in 8 high schools across 3 Michigan counties as part of a pilot cluster randomized trial. We used activity-based costing, mapping key Enhanced REP activities across implementation phases. We included multiple perspectives, including state agencies, regional education service agencies, lead organization, and implementers. We also conducted a budget impact analysis (BIA, assessing the potential financial impact of adopting Enhanced REP) and a scenario analysis to estimate replication and account for cost variability. We used an experimental embedded mixed methods approach, conducting semi-structured interviews and collecting field notes during the trial to expand and explain the cost data and the implications of costs across relevant perspectives. RESULTS Based on trial results, we estimate costs for deploying Enhanced REP are $11,903/school, with an estimated range between $8263/school and $15,201/school. We estimate that adding four additional schools, consistent with the pilot, would cost $8659/school. Qualitative results indicated misalignment in school and teacher priorities in some cases. Implementation activities, including training and implementation facilitation with the health coordinator, were sometimes in addition to regular teaching responsibilities. The extent to which this occurred was partly due to leadership priorities (e.g., sticking to the district PD schedule) and organizational priorities (e.g., budget). CONCLUSIONS Previous research findings indicate that, from a societal perspective, universal prevention is an excellent return on investment. However, notable misalignment in cost burden and priorities exists across shareholder groups. Our results indicate significant personal time costs by teachers when engaging in implementation strategy activities that impose an opportunity cost. Additional strategies are needed to improve the alignment of costs and benefits to enhance the success and sustainability of implementation. We focus on those perspectives informed by the analysis and discuss opportunities to expand a multi-level focus and create greater alignment across perspectives. TRIAL REGISTRATION ClinicalTrials.gov NCT04752189. Registered on 12 February 2021.
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Affiliation(s)
- Andria B Eisman
- Division of Kinesiology, Health, and Sport Studies, College of Education, Wayne State University, 2153 Faculty/Administration Building, 656 West Kirby Street, Detroit, MI, 48202, USA.
| | - Jacob Whitman
- Department of Economics, College of Liberal Arts, Wayne State University, 656 West Kirby Street, Detroit, MI, 48202, USA
| | - Lawrence A Palinkas
- School of Social Work, University of Southern California, 669 W 34th Street, Los Angeles, CA, 90089, USA
| | - Judy Fridline
- Genesee Intermediate School District, 2143 Maple Road, Flint, MI, 48507, USA
| | - Christina Harvey
- Oakland Intermediate School District, 2111 Pontiac Lake Road, Waterford Township, MI, 48328, USA
| | - Amy M Kilbourne
- VA Ann Arbor Healthcare System, North Campus Research Complex, 2800 Plymouth Road, Bldg 16, Ann Arbor, MI, 48109, USA
| | - David W Hutton
- Department of Health Management and Policy, School of Public Health, University of Michigan, M3525 SPH II, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
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Smith NR, Simione M, Farrar-Muir H, Granadeno J, Moreland JW, Wallace J, Frost HM, Young J, Craddock C, Sease K, Hambidge SJ, Taveras EM, Levy DE. Costs to Implement a Pediatric Weight Management Program Across 3 Distinct Contexts. Med Care 2023; 61:715-725. [PMID: 37943527 PMCID: PMC10478682 DOI: 10.1097/mlr.0000000000001891] [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] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
BACKGROUND The Connect for Health program is an evidence-based program that aligns with national recommendations for pediatric weight management and includes clinical decision support, educational handouts, and community resources. As implementation costs are a major driver of program adoption and maintenance decisions, we assessed the costs to implement the Connect for Health program across 3 health systems that primarily serve low-income communities with a high prevalence of childhood obesity. METHODS We used time-driven activity-based costing methods. Each health system (site) developed a process map and a detailed report of all implementation actions taken, aligned with major implementation requirements (eg, electronic health record integration) or strategies (eg, providing clinician training). For each action, sites identified the personnel involved and estimated the time they spent, allowing us to estimate the total costs of implementation and breakdown costs by major implementation activities. RESULTS Process maps indicated that the program integrated easily into well-child visits. Overall implementation costs ranged from $77,103 (Prisma Health) to $84,954 (Denver Health) to $142,721 (Massachusetts General Hospital). Across implementation activities, setting up the technological aspects of the program was a major driver of costs. Other cost drivers included training, engaging stakeholders, and audit and feedback activities, though there was variability across systems based on organizational context and implementation choices. CONCLUSIONS Our work highlights the major cost drivers of implementing the Connect for Health program. Accounting for context-specific considerations when assessing the costs of implementation is crucial, especially to facilitate accurate projections of implementation costs in future settings.
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Affiliation(s)
- Natalie Riva Smith
- Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health
- Mongan Institute Health Policy Research Center, Massachusetts General Hospital
| | - Meg Simione
- Division of General Academic Pediatrics, Department of Pediatrics, Mass General for Children
- Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Haley Farrar-Muir
- Division of General Academic Pediatrics, Department of Pediatrics, Mass General for Children
| | - Jazmin Granadeno
- Division of General Academic Pediatrics, Department of Pediatrics, Mass General for Children
| | | | | | - Holly M. Frost
- Department of Pediatrics, Denver Health
- Center for Health Systems Research, Denver Health, Denver
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO
| | | | - Cassie Craddock
- Department of Ambulatory Quality and Reliability, Prisma Health
| | - Kerry Sease
- Department of Pediatrics, University of South Carolina School of Medicine
- Prisma Health Children’s Hospital, Greenville, SC
| | - Simon J. Hambidge
- Ambulatory Care Services, Denver Health, Denver
- Harvard Medical School, Boston, MA
| | - Elsie M. Taveras
- Division of General Academic Pediatrics, Department of Pediatrics, Mass General for Children
- Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Douglas E. Levy
- Mongan Institute Health Policy Research Center, Massachusetts General Hospital
- Harvard Medical School, Boston, MA
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Panca M, Blackstone J, Stirrup O, Cutino-Moguel MT, Thomson E, Peters C, Snell LB, Nebbia G, Holmes A, Chawla A, Machin N, Taha Y, Mahungu T, Saluja T, de Silva TI, Saeed K, Pope C, Shin GY, Williams R, Darby A, Smith DL, Loose M, Robson SC, Laing K, Partridge DG, Price JR, Breuer J. Evaluating the cost implications of integrating SARS-CoV-2 genome sequencing for infection prevention and control investigation of nosocomial transmission within hospitals. J Hosp Infect 2023; 139:23-32. [PMID: 37308063 PMCID: PMC10257337 DOI: 10.1016/j.jhin.2023.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/30/2023] [Accepted: 06/01/2023] [Indexed: 06/14/2023]
Abstract
BACKGROUND The COG-UK hospital-onset COVID-19 infection (HOCI) trial evaluated the impact of SARS-CoV-2 whole-genome sequencing (WGS) on acute infection, prevention, and control (IPC) investigation of nosocomial transmission within hospitals. AIM To estimate the cost implications of using the information from the sequencing reporting tool (SRT), used to determine likelihood of nosocomial infection in IPC practice. METHODS A micro-costing approach for SARS-CoV-2 WGS was conducted. Data on IPC management resource use and costs were collected from interviews with IPC teams from 14 participating sites and used to assign cost estimates for IPC activities as collected in the trial. Activities included IPC-specific actions following a suspicion of healthcare-associated infection (HAI) or outbreak, as well as changes to practice following the return of data via SRT. FINDINGS The mean per-sample costs of SARS-CoV-2 sequencing were estimated at £77.10 for rapid and £66.94 for longer turnaround phases. Over the three-month interventional phases, the total management costs of IPC-defined HAIs and outbreak events across the sites were estimated at £225,070 and £416,447, respectively. The main cost drivers were bed-days lost due to ward closures because of outbreaks, followed by outbreak meetings and bed-days lost due to cohorting contacts. Actioning SRTs, the cost of HAIs increased by £5,178 due to unidentified cases and the cost of outbreaks decreased by £11,246 as SRTs excluded hospital outbreaks. CONCLUSION Although SARS-CoV-2 WGS adds to the total IPC management cost, additional information provided could balance out the additional cost, depending on identified design improvements and effective deployment.
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Affiliation(s)
- M Panca
- Comprehensive Clinical Trials Unit, Institute of Clinical Trials and Methodology, UCL, London, UK.
| | - J Blackstone
- Comprehensive Clinical Trials Unit, Institute of Clinical Trials and Methodology, UCL, London, UK
| | - O Stirrup
- Institute for Global Health, UCL, London, UK
| | | | - E Thomson
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - C Peters
- NHS Greater Glasgow and Clyde, Glasgow, UK
| | - L B Snell
- Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - G Nebbia
- Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - A Holmes
- Imperial College Healthcare NHS Trust, London, UK
| | - A Chawla
- Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - N Machin
- Manchester University NHS Foundation Trust, Manchester, UK
| | - Y Taha
- Departments of Virology and Infectious Diseases, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle, UK
| | - T Mahungu
- Royal Free NHS Foundation Trust, London, UK
| | - T Saluja
- Sandwell and West Birmingham NHS Trust, UK
| | - T I de Silva
- Department of Infection, Immunity and Cardiovascular Disease, Medical School, The University of Sheffield, Sheffield, UK
| | - K Saeed
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - C Pope
- St George's University Hospitals NHS Foundation Trust, London, UK; Institute for Infection and Immunity, St George's University of London, London, UK
| | - G Y Shin
- University College London Hospitals NHS Foundation Trust, London, UK
| | - R Williams
- Department of Genetics & Genomic Medicine, UCL Great Ormond Street Institute of Child Health, UCL, London, UK
| | - A Darby
- Centre for Genomic Research, University of Liverpool, Liverpool, UK
| | - D L Smith
- Department of Applied Sciences, Northumbria University, Newcastle, UK
| | - M Loose
- School of Life Sciences, University of Nottingham, Nottingham, UK
| | - S C Robson
- Centre for Enzyme Innovation & School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, UK
| | - K Laing
- Institute for Infection and Immunity, St George's University of London, London, UK
| | - D G Partridge
- Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - J R Price
- Imperial College Healthcare NHS Trust, London, UK
| | - J Breuer
- Department of Infection, Immunity and Inflammation, Great Ormond Street Institute of Child Health, UCL, London, UK
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Smith NR, Knocke KE, Hassmiller Lich K. Using decision analysis to support implementation planning in research and practice. Implement Sci Commun 2022; 3:83. [PMID: 35907894 PMCID: PMC9338582 DOI: 10.1186/s43058-022-00330-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 07/12/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The process of implementing evidence-based interventions, programs, and policies is difficult and complex. Planning for implementation is critical and likely plays a key role in the long-term impact and sustainability of interventions in practice. However, implementation planning is also difficult. Implementors must choose what to implement and how best to implement it, and each choice has costs and consequences to consider. As a step towards supporting structured and organized implementation planning, we advocate for increased use of decision analysis. MAIN TEXT When applied to implementation planning, decision analysis guides users to explicitly define the problem of interest, outline different plans (e.g., interventions/actions, implementation strategies, timelines), and assess the potential outcomes under each alternative in their context. We ground our discussion of decision analysis in the PROACTIVE framework, which guides teams through key steps in decision analyses. This framework includes three phases: (1) definition of the decision problems and overall objectives with purposeful stakeholder engagement, (2) identification and comparison of different alternatives, and (3) synthesis of information on each alternative, incorporating uncertainty. We present three examples to illustrate the breadth of relevant decision analysis approaches to implementation planning. CONCLUSION To further the use of decision analysis for implementation planning, we suggest areas for future research and practice: embrace model thinking; build the business case for decision analysis; identify when, how, and for whom decision analysis is more or less useful; improve reporting and transparency of cost data; and increase collaborative opportunities and training.
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Affiliation(s)
- Natalie Riva Smith
- Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, 02115, USA.
| | - Kathleen E Knocke
- Department of Health Policy and Management, Gillings School of Global Public Health, UNC Chapel Hill, Chapel Hill, USA
| | - Kristen Hassmiller Lich
- Department of Health Policy and Management, Gillings School of Global Public Health, UNC Chapel Hill, Chapel Hill, USA
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Haber Y, Fu SS, Rogers E, Richter K, Tenner C, Dognin J, Goldfeld K, Gold HT, Sherman SE. A novel opt-in vs opt-out approach to referral-based treatment of tobacco use in Veterans Affairs (VA) primary care clinics: A provider-level randomized controlled trial protocol. Contemp Clin Trials 2022; 116:106716. [PMID: 35276337 DOI: 10.1016/j.cct.2022.106716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/17/2022] [Accepted: 02/23/2022] [Indexed: 11/18/2022]
Abstract
To determine whether an opt-out approach is effective for referral to treatment for tobacco use, we designed a clinical reminder for nurses in a primary care setting that provides a referral for patients who smoke cigarettes. We will use a two-arm, cluster-randomized design to assign nurses at the VA New York Harbor Healthcare System to test which mode of referral (opt-in vs opt-out) is more effective. All patients will be referred to evidence-based treatment for tobacco cessation including counseling from the New York State Quitline, and VetsQuit, a text messaging-based system for tobacco cessation counseling. We will measure patient engagement with the referral both in the short and long term to determine if referral modality had an impact on tobacco cessation treatment. We will also measure nurse engagement with the referral before, during, and after the implementation of the reminder to determine whether an opt-out approach is cost effective at the health system level. At the conclusion of this project, we expect to have developed and tested an opt-out system for increasing tobacco cessation treatment for Veterans in VA primary care and to have a thorough understanding of factors associated with implementation. Trial Registration:Clinicaltrials.govIdentifierNCT03477435.
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Affiliation(s)
- Yaa Haber
- VA New York Harbor Healthcare System, New York, NY, USA; Department of Medicine, VA New York Harbor Healthcare System, New York, NY, USA; NYU Grossman School of Medicine, Department of Population Health, New York, NY, USA.
| | - Steven S Fu
- Department of Medicine, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Erin Rogers
- VA New York Harbor Healthcare System, New York, NY, USA; NYU Grossman School of Medicine, Department of Population Health, New York, NY, USA
| | - Kim Richter
- Department of Preventive Medicine and Public Health, University of Kansas Medical Center, Kansas City, KS, USA
| | - Craig Tenner
- VA New York Harbor Healthcare System, New York, NY, USA; NYU Grossman School of Medicine, Department of Medicine, New York, NY, USA
| | - Joanna Dognin
- VA New York Harbor Healthcare System, New York, NY, USA; NYU Grossman School of Medicine, Department of Medicine, New York, NY, USA; Department of Psychology, VA New York Harbor Healthcare System, New York, NY, USA
| | - Keith Goldfeld
- VA New York Harbor Healthcare System, New York, NY, USA; NYU Grossman School of Medicine, Department of Population Health, New York, NY, USA
| | - Heather T Gold
- VA New York Harbor Healthcare System, New York, NY, USA; NYU Grossman School of Medicine, Department of Population Health, New York, NY, USA
| | - Scott E Sherman
- VA New York Harbor Healthcare System, New York, NY, USA; NYU Grossman School of Medicine, Department of Population Health, New York, NY, USA; NYU Grossman School of Medicine, Department of Medicine, New York, NY, USA
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9
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Gold HT, McDermott C, Hoomans T, Wagner TH. Cost data in implementation science: categories and approaches to costing. Implement Sci 2022; 17:11. [PMID: 35090508 PMCID: PMC8796347 DOI: 10.1186/s13012-021-01172-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 11/03/2021] [Indexed: 12/14/2022] Open
Abstract
A lack of cost information has been cited as a barrier to implementation and a limitation of implementation research. This paper explains how implementation researchers might optimize their measurement and inclusion of costs, building on traditional economic evaluations comparing costs and effectiveness of health interventions. The objective of all economic evaluation is to inform decision-making for resource allocation and to measure costs that reflect opportunity costs—the value of resource inputs in their next best alternative use, which generally vary by decision-maker perspective(s) and time horizon(s). Analyses that examine different perspectives or time horizons must consider cost estimation accuracy, because over longer time horizons, all costs are variable; however, with shorter time horizons and narrower perspectives, one must differentiate the fixed and variable costs, with fixed costs generally excluded from the evaluation. This paper defines relevant costs, identifies sources of cost data, and discusses cost relevance to potential decision-makers contemplating or implementing evidence-based interventions. Costs may come from the healthcare sector, informal healthcare sector, patient, participant or caregiver, and other sectors such as housing, criminal justice, social services, and education. Finally, we define and consider the relevance of costs by phase of implementation and time horizon, including pre-implementation and planning, implementation, intervention, downstream, and adaptation, and through replication, sustainment, de-implementation, or spread.
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Affiliation(s)
- Kathleen Knocke
- Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Todd W Wagner
- Health Economics Resource Center, VA Palo Alto Health Care System, Palo Alto, California, USA .,Surgery, Stanford University School of Medicine, Stanford, California, USA
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Rusch A, DeCamp LM, Liebrecht CM, Choi SY, Dalack GW, Kilbourne AM, Smith SN. A Roadmap to Inform the Implementation of Evidence-Based Collaborative Care Interventions in Communities: Insights From the Michigan Mental Health Integration Partnership. Front Public Health 2021; 9:655999. [PMID: 34109147 PMCID: PMC8180904 DOI: 10.3389/fpubh.2021.655999] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 04/26/2021] [Indexed: 11/30/2022] Open
Abstract
Background: Despite increasing calls for further spread of evidence-based collaborative care interventions (EBIs) in community-based settings, practitioner-driven efforts are often stymied by a lack of experience in addressing barriers to community-based implementation, especially for those not familiar with implementation science. The Michigan Mental Health Integration Partnership (MIP) is a statewide initiative that funds projects that support implementation and uptake of EBIs in community-based settings. MIP also provides an in situ implementation laboratory for understanding barriers to the uptake of EBIs across a variety of settings. We report findings from a statewide qualitative study of practitioners involved in MIP projects to garner their perspectives of best practices in the implementation of EBIs. Methods: Twenty-eight semi-structured interviews of practitioners and researchers from six MIP Projects were conducted with individuals implementing various MIP EBI projects across Michigan, including stakeholders from project teams, implementation sites, and the State of Michigan, to identify common barriers, challenges, and implementation strategies deployed by the project teams, with the purpose of informing a set of implementation steps and milestones. Results: Stakeholders identified a number of barriers to and strategies for success, including the need for tailoring program deployment and implementation to specific site needs, development of web-based tools for facilitating program implementation, and the importance of upper-level administration buy-in. Findings informed our resultant community-based Implementation Roadmap, which identifies critical steps across three implementation phases—pre-implementation, implementation, and sustainability—for implementation practitioners to use in their EBI implementation efforts. Conclusion: Implementation practitioners interested in community-based EBI implementation often lack access to operationalized implementation “steps” or “best practices” that can facilitate successful uptake and evaluation. Our community-informed MIP Implementation Roadmap, offering generalized steps for reaching successful implementation, uses experiences from a diverse set of MIP teams to guide practitioners through the practices necessary for scaling up EBIs in community-based settings over pre-implementation, implementation and sustainability phases.
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Affiliation(s)
- Amy Rusch
- Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | | | - Celeste M Liebrecht
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Seo Youn Choi
- Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - Gregory W Dalack
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Amy M Kilbourne
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States.,U.S. Department of Veterans Affairs, Quality Enhancement Research Initiative, Washington, DC, United States
| | - Shawna N Smith
- Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, MI, United States.,Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States.,Institute for Social Research, University of Michigan, Ann Arbor, MI, United States
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