1
|
Kukhareva PV, Li H, Caverly TJ, Fagerlin A, Del Fiol G, Hess R, Zhang Y, Butler JM, Schlechter C, Flynn MC, Reddy C, Choi J, Balbin C, Warner IA, Warner PB, Nanjo C, Kawamoto, K. Lung Cancer Screening Before and After a Multifaceted Electronic Health Record Intervention: A Nonrandomized Controlled Trial. JAMA Netw Open 2024; 7:e2415383. [PMID: 38848065 PMCID: PMC11161845 DOI: 10.1001/jamanetworkopen.2024.15383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 03/24/2024] [Indexed: 06/10/2024] Open
Abstract
Importance Lung cancer is the deadliest cancer in the US. Early-stage lung cancer detection with lung cancer screening (LCS) through low-dose computed tomography (LDCT) improves outcomes. Objective To assess the association of a multifaceted clinical decision support intervention with rates of identification and completion of recommended LCS-related services. Design, Setting, and Participants This nonrandomized controlled trial used an interrupted time series design, including 3 study periods from August 24, 2019, to April 27, 2022: baseline (12 months), period 1 (11 months), and period 2 (9 months). Outcome changes were reported as shifts in the outcome level at the beginning of each period and changes in monthly trend (ie, slope). The study was conducted at primary care and pulmonary clinics at a health care system headquartered in Salt Lake City, Utah, among patients aged 55 to 80 years who had smoked 30 pack-years or more and were current smokers or had quit smoking in the past 15 years. Data were analyzed from September 2023 through February 2024. Interventions Interventions in period 1 included clinician-facing preventive care reminders, an electronic health record-integrated shared decision-making tool, and narrative LCS guidance provided in the LDCT ordering screen. Interventions in period 2 included the same clinician-facing interventions and patient-facing reminders for LCS discussion and LCS. Main Outcome and Measure The primary outcome was LCS care gap closure, defined as the identification and completion of recommended care services. LCS care gap closure could be achieved through LDCT completion, other chest CT completion, or LCS shared decision-making. Results The study included 1865 patients (median [IQR] age, 64 [60-70] years; 759 female [40.7%]). The clinician-facing intervention (period 1) was not associated with changes in level but was associated with an increase in slope of 2.6 percentage points (95% CI, 2.4-2.7 percentage points) per month in care gap closure through any means and 1.6 percentage points (95% CI, 1.4-1.8 percentage points) per month in closure through LDCT. In period 2, introduction of patient-facing reminders was associated with an immediate increase in care gap closure (2.3 percentage points; 95% CI, 1.0-3.6 percentage points) and closure through LDCT (2.4 percentage points; 95% CI, 0.9-3.9 percentage points) but was not associated with an increase in slope. The overall care gap closure rate was 175 of 1104 patients (15.9%) at the end of the baseline period vs 588 of 1255 patients (46.9%) at the end of period 2. Conclusions and Relevance In this study, a multifaceted intervention was associated with an improvement in LCS care gap closure. Trial Registration ClinicalTrials.gov Identifier: NCT04498052.
Collapse
Affiliation(s)
| | - Haojia Li
- Study Design and Biostatistics Center, University of Utah, Salt Lake City
| | - Tanner J. Caverly
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, Michigan
- Department of Learning Health Sciences, University of Michigan, Ann Arbor
- Department of Internal Medicine, University of Michigan, Ann Arbor
| | - Angela Fagerlin
- Department of Population Health Sciences, University of Utah, Salt Lake City
- Salt Lake City VA Informatics Decision-Enhancement and Analytic Sciences Center for Innovation, Salt Lake City, Utah
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City
| | - Rachel Hess
- Department of Population Health Sciences, University of Utah, Salt Lake City
- Department of Internal Medicine, University of Utah, Salt Lake City
| | - Yue Zhang
- Study Design and Biostatistics Center, University of Utah, Salt Lake City
| | - Jorie M. Butler
- Department of Biomedical Informatics, University of Utah, Salt Lake City
- Department of Internal Medicine, University of Utah, Salt Lake City
- Geriatrics Research and Education Center, George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, Utah
| | - Chelsey Schlechter
- Department of Population Health Sciences, University of Utah, Salt Lake City
| | - Michael C. Flynn
- Department of Internal Medicine, University of Utah, Salt Lake City
- Department of Pediatrics, University of Utah, Salt Lake City
- Community Physicians Group, University of Utah Health, Salt Lake City
| | - Chakravarthy Reddy
- Study Design and Biostatistics Center, University of Utah, Salt Lake City
| | - Joshua Choi
- Department of Biomedical Informatics, University of Utah, Salt Lake City
| | - Christian Balbin
- Department of Biomedical Informatics, University of Utah, Salt Lake City
| | - Isaac A. Warner
- Department of Biomedical Informatics, University of Utah, Salt Lake City
| | - Phillip B. Warner
- Department of Biomedical Informatics, University of Utah, Salt Lake City
| | - Claude Nanjo
- Department of Biomedical Informatics, University of Utah, Salt Lake City
| | - Kensaku Kawamoto,
- Department of Biomedical Informatics, University of Utah, Salt Lake City
| |
Collapse
|
2
|
Reese TJ, Domenico HJ, Hernandez A, Byrne DW, Moore RP, Williams JB, Douthit BJ, Russo E, McCoy AB, Ivory CH, Steitz BD, Wright A. Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation. JMIR Med Inform 2024; 12:e51842. [PMID: 38722209 PMCID: PMC11094428 DOI: 10.2196/51842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 03/08/2024] [Accepted: 03/10/2024] [Indexed: 05/18/2024] Open
Abstract
Background Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care. Objective To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale). Methods We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort. Results A total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803). Conclusions We developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes.
Collapse
Affiliation(s)
- Thomas J Reese
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Henry J Domenico
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Antonio Hernandez
- Department of Anesthesiology, Division of Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Daniel W Byrne
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Ryan P Moore
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jessica B Williams
- Department of Nursing, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Brian J Douthit
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Elise Russo
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Allison B McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Catherine H Ivory
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bryan D Steitz
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| |
Collapse
|
3
|
Dodd RH, Sharman AR, Yap ML, Stone E, Marshall H, Rhee J, McCullough S, Rankin NM. "We need to work towards it, whatever it takes."-participation factors in the acceptability and feasibility of lung cancer screening in Australia: the perspectives of key stakeholders. Transl Lung Cancer Res 2024; 13:240-255. [PMID: 38496699 PMCID: PMC10938089 DOI: 10.21037/tlcr-23-623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/10/2024] [Indexed: 03/19/2024]
Abstract
Background Low dose computed tomography (LDCT) screening, targeted at those at high-risk, has been shown to significantly reduce lung cancer mortality and detect cancers at an early stage. Practical, attitudinal and demographic factors can inhibit screening participation in high-risk populations. This study aimed to explore stakeholders' views about barriers and enablers (determinants) to participation in lung cancer screening (LCS) in Australia. Methods Twenty-four focus groups (range 2-5 participants) were conducted in 2021 using the Zoom platform. Participants were 84 health professionals, researchers, policy makers and program managers of current screening programs. Focus groups consisted of a structured presentation with facilitated discussion lasting about 1 hour. The content was analysed thematically and mapped to the Consolidated Framework for Implementation Research (CFIR). Results Screening determinants were identified across each stage of the proposed screening and assessment pathway. Challenges included participant factors such as encouraging participation for individuals at high-risk, whilst ensuring that access and equity issues were carefully considered in program design. The development of awareness campaigns that engaged LCS participants and health professionals, as well as streamlined referral processes for initial entry and follow-up, were strongly advocated for. Considering practical factors included the use of mobile vans in convenient locations. Conclusions Participants reported that LCS in Australia was acceptable and feasible. Participants identified a complex set of determinants across the proposed screening and assessment pathway. Strategies that enable the best chance for program success must be identified prior to implementation of a national LCS program.
Collapse
Affiliation(s)
- Rachael Helen Dodd
- The Daffodil Centre, A Joint Venture between The University of Sydney and Cancer Council New South Wales, Faculty of Medicine and Health, The University of Sydney, New South Wales, Australia
- Faculty of Medicine and Health, School of Public Health, The University of Sydney, New South Wales, Australia
| | - Ashleigh Rebecca Sharman
- Faculty of Medicine and Health, School of Public Health, The University of Sydney, New South Wales, Australia
| | - Mei Ling Yap
- Faculty of Medicine and Health, School of Public Health, The University of Sydney, New South Wales, Australia
- Collaboration for Cancer Outcomes, Research and Evaluation, Ingham Institute, University of New South Wales Sydney, Liverpool, New South Wales, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Western Sydney University, Campbelltown, New South Wales, Australia
- The George Institute, University of New South Wales Sydney, New South Wales, Australia
| | - Emily Stone
- Department of Thoracic Medicine and Lung Transplantation, St Vincent’s Hospital Sydney, New South Wales, Australia
- School of Clinical Medicine University of New South Wales Sydney, New South Wales, Australia
| | - Henry Marshall
- University of Queensland Thoracic Research Centre and Department of Thoracic Medicine, The Prince Charles Hospital, Chermside, Queensland, Australia
| | - Joel Rhee
- School of Population Health, Faculty of Medicine and Health, University of New South Wales Sydney, New South Wales, Australia
- Graduate School of Medicine, Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, New South Wales, Australia
| | - Sue McCullough
- Thoracic Oncology Group Australasia Consumer Advisory Panel, Melbourne, Victoria, Australia
| | - Nicole Marion Rankin
- Centre for Health Policy, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| |
Collapse
|
4
|
Skurla SE, Leishman NJ, Fagerlin A, Wiener RS, Lowery J, Caverly TJ. Clinician Perceptions on Using Decision Tools to Support Prediction-Based Shared Decision Making for Lung Cancer Screening. MDM Policy Pract 2024; 9:23814683241252786. [PMID: 38779527 PMCID: PMC11110512 DOI: 10.1177/23814683241252786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 04/08/2024] [Indexed: 05/25/2024] Open
Abstract
Background Considering a patient's full risk factor profile can promote personalized shared decision making (SDM). One way to accomplish this is through encounter tools that incorporate prediction models, but little is known about clinicians' perceptions of the feasibility of using these tools in practice. We examined how clinicians react to using one such encounter tool for personalizing SDM about lung cancer screening (LCS). Design We conducted a qualitative study based on field notes from academic detailing visits during a multisite quality improvement program. The detailer engaged one-on-one with 96 primary care clinicians across multiple Veterans Affairs sites (7 medical centers and 6 outlying clinics) to get feedback on 1) the rationale for prediction-based LCS and 2) how to use the DecisionPrecision (DP) encounter tool with eligible patients to personalize LCS discussions. Results Thematic content analysis from detailing visit data identified 6 categories of clinician willingness to use the DP tool to personalize SDM for LCS (adoption potential), varying from "Enthusiastic Potential Adopter" (n = 18) to "Definite Non-Adopter" (n = 16). Many clinicians (n = 52) articulated how they found the concept of prediction-based SDM highly appealing. However, to varying degrees, nearly all clinicians identified challenges to incorporating such an approach in routine practice. Limitations The results are based on the clinician's initial reactions rather than longitudinal experience. Conclusions While many primary care clinicians saw real value in using prediction to personalize LCS decisions, more support is needed to overcome barriers to using encounter tools in practice. Based on these findings, we propose several strategies that may facilitate the adoption of prediction-based SDM in contexts such as LCS. Highlights Encounter tools that incorporate prediction models promote personalized shared decision making (SDM), but little is known about clinicians' perceptions of the feasibility of using these tools in practice.We examined how clinicians react to using one such encounter tool for personalizing SDM about lung cancer screening (LCS).While many clinicians found the concept of prediction-based SDM highly appealing, nearly all clinicians identified challenges to incorporating such an approach in routine practice.We propose several strategies to overcome adoption barriers and facilitate the use of prediction-based SDM in contexts such as LCS.
Collapse
Affiliation(s)
- Sarah E. Skurla
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, MI, USA
| | | | - Angela Fagerlin
- University of Utah School of Medicine, Salt Lake City, UT, USA
- Informatics Decision-Enhancement and Analytic Sciences (IDEAS) Center for Innovation, VA Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Renda Soylemez Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA, USA
- The Pulmonary Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Julie Lowery
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, MI, USA
| | - Tanner J. Caverly
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, MI, USA
- Department of Learning Health Sciences, University of Michigan School of Medicine, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| |
Collapse
|
5
|
Kukhareva PV, Li H, Caverly TJ, Del Fiol G, Fagerlin A, Butler JM, Hess R, Zhang Y, Taft T, Flynn MC, Reddy C, Martin DK, Warner IA, Rodriguez-Loya S, Warner PB, Kawamoto K. Implementation of Lung Cancer Screening in Primary Care and Pulmonary Clinics: Pragmatic Clinical Trial of Electronic Health Record-Integrated Everyday Shared Decision-Making Tool and Clinician-Facing Prompts. Chest 2023; 164:1325-1338. [PMID: 37142092 PMCID: PMC10792294 DOI: 10.1016/j.chest.2023.04.040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 04/18/2023] [Accepted: 04/25/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Although low-dose CT (LDCT) scan imaging lung cancer screening (LCS) can reduce lung cancer mortality, it remains underused. Shared decision-making (SDM) is recommended to assess the balance of benefits and harms for each patient. RESEARCH QUESTION Do clinician-facing electronic health record (EHR) prompts and an EHR-integrated everyday SDM tool designed to support routine incorporation of SDM into primary care improve LDCT scan imaging ordering and completion? STUDY DESIGN AND METHODS A preintervention and postintervention analysis was conducted in 30 primary care and four pulmonary clinics for visits with patients who met United States Preventive Services Task Force criteria for LCS. Propensity scores were used to adjust for covariates. Subgroup analyses were conducted based on the expected benefit from screening (high benefit vs intermediate benefit), pulmonologist involvement (ie, whether the patient was seen in a pulmonary clinic in addition to a primary care clinic), sex, and race and ethnicity. RESULTS In the 12-month preintervention phase among 1,090 eligible patients, 77 patients (7.1%) had LDCT scan imaging orders and 48 patients (4.4%) completed screenings. In the 9-month intervention phase among 1,026 eligible patients, 280 patients (27.3%) had LDCT scan imaging orders and 182 patients (17.7%) completed screenings. Adjusted ORs were 4.9 (95% CI, 3.4-6.9; P < .001) and 4.7 (95% CI, 3.1-7.1; P < .001) for LDCT imaging ordering and completion, respectively. Subgroup analyses showed increases in ordering and completion for all patient subgroups. In the intervention phase, the SDM tool was used by 23 of 102 ordering providers (22.5%) and for 69 of 274 patients (25.2%) for whom LDCT scan imaging was ordered and who needed SDM at the time of ordering. INTERPRETATION Clinician-facing EHR prompts and an EHR-integrated everyday SDM tool are promising approaches to improving LCS in the primary care setting. However, room for improvement remains. As such, further research is warranted. TRIAL REGISTRY ClinicalTrials.gov; No.: NCT04498052; URL: www. CLINICALTRIALS gov.
Collapse
Affiliation(s)
- Polina V Kukhareva
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | - Haojia Li
- Study Design and Biostatistics Center, University of Utah, Salt Lake City, UT
| | - Tanner J Caverly
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, MI; Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI; Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | - Angela Fagerlin
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT; Salt Lake City VA Informatics Decision-Enhancement and Analytic Sciences (IDEAS) Center for Innovation, Salt Lake City, UT
| | - Jorie M Butler
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT; Department of Internal Medicine, University of Utah, Salt Lake City, UT; Geriatrics Research and Education Center, George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT
| | - Rachel Hess
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT; Department of Internal Medicine, University of Utah, Salt Lake City, UT
| | - Yue Zhang
- Study Design and Biostatistics Center, University of Utah, Salt Lake City, UT
| | - Teresa Taft
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | - Michael C Flynn
- Department of Internal Medicine, University of Utah, Salt Lake City, UT; Department of Pediatrics, University of Utah, Salt Lake City, UT; Community Physicians Group, University of Utah Health, Salt Lake City, UT
| | | | - Douglas K Martin
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | - Isaac A Warner
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | | | - Phillip B Warner
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.
| |
Collapse
|
6
|
Kim B, Cruden G, Crable EL, Quanbeck A, Mittman BS, Wagner AD. A structured approach to applying systems analysis methods for examining implementation mechanisms. Implement Sci Commun 2023; 4:127. [PMID: 37858215 PMCID: PMC10588196 DOI: 10.1186/s43058-023-00504-5] [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] [Received: 04/08/2023] [Accepted: 09/23/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND It is challenging to identify and understand the specific mechanisms through which an implementation strategy affects implementation outcomes, as implementation happens in the context of complex, multi-level systems. These systems and the mechanisms within each level have their own dynamic environments that change frequently. For instance, sequencing may matter in that a mechanism may only be activated indirectly by a strategy through another mechanism. The dosage or strength of a mechanism may vary over time or across different health care system levels. To elucidate the mechanisms relevant to successful implementation amidst this complexity, systems analysis methods are needed to model and manage complexity. METHODS The fields of systems engineering and systems science offer methods-which we refer to as systems analysis methods-to help explain the interdependent relationships between and within systems, as well as dynamic changes to systems over time. When applied to studying implementation mechanisms, systems analysis methods can help (i) better identify and manage unknown conditions that may or may not activate mechanisms (both expected mechanisms targeted by a strategy and unexpected mechanisms that the methods help detect) and (ii) flexibly guide strategy adaptations to address contextual influences that emerge after the strategy is selected and used. RESULTS In this paper, we delineate a structured approach to applying systems analysis methods for examining implementation mechanisms. The approach includes explicit steps for selecting, tailoring, and evaluating an implementation strategy regarding the mechanisms that the strategy is initially hypothesized to activate, as well as additional mechanisms that are identified through the steps. We illustrate the approach using a case example. We then discuss the strengths and limitations of this approach, as well as when these steps might be most appropriate, and suggest work to further the contributions of systems analysis methods to implementation mechanisms research. CONCLUSIONS Our approach to applying systems analysis methods can encourage more mechanisms research efforts to consider these methods and in turn fuel both (i) rigorous comparisons of these methods to alternative mechanisms research approaches and (ii) an active discourse across the field to better delineate when these methods are appropriate for advancing mechanisms-related knowledge.
Collapse
Affiliation(s)
- Bo Kim
- VA Boston Healthcare System, 150 South Huntington Avenue, Boston, MA, 02130, USA.
- Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA.
| | - Gracelyn Cruden
- Chestnut Health Systems, Lighthouse Institute-Oregon Group, 1255 Pearl Street, Eugene, OR, 97401, USA
| | - Erika L Crable
- UC San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
- Child and Adolescent Services Research Center, 3665 Kearny Villa Road, San Diego, CA, 92123, USA
- UC San Diego ACTRI Dissemination and Implementation Science Center, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Andrew Quanbeck
- University of Wisconsin-Madison, 610 North Whitney Way, Madison, WI, 53705, USA
| | - Brian S Mittman
- Kaiser Permanente Southern California, 200 North Lewis Street, Orange, CA, 92868, USA
- University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90089, USA
- UCLA, 405 Hilgard Avenue, Los Angeles, CA, 90095, USA
| | - Anjuli D Wagner
- University of Washington, 3980 15Th Avenue NE, Seattle, WA, 98195, USA
| |
Collapse
|
7
|
Herrera DJ, van de Veerdonk W, Berhe NM, Talboom S, van Loo M, Alejos AR, Ferrari A, Van Hal G. Mixed-Method Systematic Review and Meta-Analysis of Shared Decision-Making Tools for Cancer Screening. Cancers (Basel) 2023; 15:3867. [PMID: 37568683 PMCID: PMC10417450 DOI: 10.3390/cancers15153867] [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] [Received: 06/01/2023] [Revised: 07/22/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
This review aimed to synthesize evidence on the effectiveness of shared decision-making (SDM) tools for cancer screening and explored the preferences of vulnerable people and clinicians regarding the specific characteristics of the SDM tools. A mixed-method convergent segregated approach was employed, which involved an independent synthesis of quantitative and qualitative data. Articles were systematically selected and screened, resulting in the inclusion and critical appraisal of 55 studies. Results from the meta-analysis revealed that SDM tools were more effective for improving knowledge, reducing decisional conflict, and increasing screening intentions among vulnerable populations compared to non-vulnerable populations. Subgroup analyses showed minimal heterogeneity for decisional conflict outcomes measured over a six-month period. Insights from the qualitative findings revealed the complexities of clinicians' and vulnerable populations' preferences for an SDM tool in cancer screening. Vulnerable populations highly preferred SDM tools with relevant information, culturally tailored content, and appropriate communication strategies. Clinicians, on the other hand, highly preferred tools that can be easily integrated into their medical systems for efficient use and can effectively guide their practice for cancer screening while considering patients' values. Considering the complexities of patients' and clinicians' preferences in SDM tool characteristics, fostering collaboration between patients and clinicians during the creation of an SDM tool for cancer screening is essential. This collaboration may ensure effective communication about the specific tool characteristics that best support the needs and preferences of both parties.
Collapse
Affiliation(s)
- Deborah Jael Herrera
- Social Epidemiology and Health Policy (SEHPO), Family Medicine and Population Health (FAMPOP) Department, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, 2610 Antwerp, Belgium
| | - Wessel van de Veerdonk
- Social Epidemiology and Health Policy (SEHPO), Family Medicine and Population Health (FAMPOP) Department, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, 2610 Antwerp, Belgium
- Expertise Unit People and Wellbeing, Campus Zandpoortvest Thomas More University of Applied Sciences, 2800 Mechelen, Belgium
| | - Neamin M Berhe
- Social Epidemiology and Health Policy (SEHPO), Family Medicine and Population Health (FAMPOP) Department, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, 2610 Antwerp, Belgium
- Société Générale de Surveillance (SGS), 2800 Mechelen, Belgium
| | - Sarah Talboom
- Expertise Unit People and Wellbeing, Campus Zandpoortvest Thomas More University of Applied Sciences, 2800 Mechelen, Belgium
| | - Marlon van Loo
- Expertise Unit People and Wellbeing, Campus Zandpoortvest Thomas More University of Applied Sciences, 2800 Mechelen, Belgium
| | - Andrea Ruiz Alejos
- Social Epidemiology and Health Policy (SEHPO), Family Medicine and Population Health (FAMPOP) Department, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, 2610 Antwerp, Belgium
| | - Allegra Ferrari
- Social Epidemiology and Health Policy (SEHPO), Family Medicine and Population Health (FAMPOP) Department, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, 2610 Antwerp, Belgium
- Department of Health Sciences (DISSAL), University of Genoa, Via Pastore 1, 16123 Genoa, Italy
| | - Guido Van Hal
- Social Epidemiology and Health Policy (SEHPO), Family Medicine and Population Health (FAMPOP) Department, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, 2610 Antwerp, Belgium
| |
Collapse
|
8
|
Wiener RS, Gould MK. Selecting Candidates for Lung Cancer Screening: Implications for Effectiveness, Efficiency, Equity, and Implementation. Ann Intern Med 2023; 176:413-414. [PMID: 36745888 DOI: 10.7326/m23-0230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Affiliation(s)
- Renda Soylemez Wiener
- Center for Health Organization and Implementation Research, VA Boston Healthcare System, Boston, and The Pulmonary Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Michael K Gould
- Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California
| |
Collapse
|
9
|
Santore LA, Novotny S, Tseng R, Patel M, Albano D, Dhamija A, Tannous H, Nemesure B, Shroyer KR, Bilfinger T. Morphologic Severity of Atypia Is Predictive of Lung Cancer Diagnosis. Cancers (Basel) 2023; 15:cancers15020397. [PMID: 36672346 PMCID: PMC9857279 DOI: 10.3390/cancers15020397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 12/27/2022] [Accepted: 12/31/2022] [Indexed: 01/11/2023] Open
Abstract
In cytologic analysis of lung nodules, specimens classified as atypia cannot be definitively diagnosed as benign or malignant. Atypia patients are typically subject to additional procedures to obtain repeat samples, thus delaying diagnosis. We evaluate morphologic categories predictive of lung cancer in atypia patients. This retrospective study stratified patients evaluated for primary lung nodules based on cytologic diagnoses. Atypia patients were further stratified based on the most severe verbiage used to describe the atypical cytology. Logistic regressions and receiver operator characteristic curves were performed. Of 129 patients with cytologic atypia, 62.8% later had cytologically or histologically confirmed lung cancer and 37.2% had benign respiratory processes. Atypia severity significantly predicted final diagnosis even while controlling for pack years and modified Herder score (p = 0.012). Pack years, atypia severity, and modified Herder score predicted final diagnosis independently and while adjusting for covariates (all p < 0.001). This model generated a significantly improved area under the curve compared to pack years, atypia severity, and modified Herder score (all p < 0.001) alone. Patients with severe atypia may benefit from repeat sampling for cytologic confirmation within one month due to high likelihood of malignancy, while those with milder atypia may be followed clinically.
Collapse
Affiliation(s)
- Lee Ann Santore
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
- Correspondence:
| | - Samantha Novotny
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Robert Tseng
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
- Yale School of Medicine, Yale University, New Haven, CT 06520, USA
| | - Mit Patel
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Denise Albano
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
- Stony Brook Chest Clinic, Stony Brook University Hospital, Stony Brook, NY 11794, USA
| | - Ankit Dhamija
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
- Stony Brook Chest Clinic, Stony Brook University Hospital, Stony Brook, NY 11794, USA
- Department of Surgery, Stony Brook University, Stony Brook, NY 11794, USA
| | - Henry Tannous
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
- Stony Brook Chest Clinic, Stony Brook University Hospital, Stony Brook, NY 11794, USA
- Department of Surgery, Stony Brook University, Stony Brook, NY 11794, USA
| | - Barbara Nemesure
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
- Department of Family, Population and Preventive, Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Kenneth R. Shroyer
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
- Department of Pathology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Thomas Bilfinger
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
- Stony Brook Chest Clinic, Stony Brook University Hospital, Stony Brook, NY 11794, USA
- Department of Surgery, Stony Brook University, Stony Brook, NY 11794, USA
| |
Collapse
|
10
|
Reese TJ, Liu S, Steitz B, McCoy A, Russo E, Koh B, Ancker J, Wright A. Conceptualizing clinical decision support as complex interventions: a meta-analysis of comparative effectiveness trials. J Am Med Inform Assoc 2022; 29:1744-1756. [PMID: 35652167 PMCID: PMC9471719 DOI: 10.1093/jamia/ocac089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/26/2022] [Accepted: 05/23/2022] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVES Complex interventions with multiple components and behavior change strategies are increasingly implemented as a form of clinical decision support (CDS) using native electronic health record functionality. Objectives of this study were, therefore, to (1) identify the proportion of randomized controlled trials with CDS interventions that were complex, (2) describe common gaps in the reporting of complexity in CDS research, and (3) determine the impact of increased complexity on CDS effectiveness. MATERIALS AND METHODS To assess CDS complexity and identify reporting gaps for characterizing CDS interventions, we used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting tool for complex interventions. We evaluated the effect of increased complexity using random-effects meta-analysis. RESULTS Most included studies evaluated a complex CDS intervention (76%). No studies described use of analytical frameworks or causal pathways. Two studies discussed use of theory but only one fully described the rationale and put it in context of a behavior change. A small but positive effect (standardized mean difference, 0.147; 95% CI, 0.039-0.255; P < .01) in favor of increasing intervention complexity was observed. DISCUSSION While most CDS studies should classify interventions as complex, opportunities persist for documenting and providing resources in a manner that would enable CDS interventions to be replicated and adapted. Unless reporting of the design, implementation, and evaluation of CDS interventions improves, only slight benefits can be expected. CONCLUSION Conceptualizing CDS as complex interventions may help convey the careful attention that is needed to ensure these interventions are contextually and theoretically informed.
Collapse
Affiliation(s)
- Thomas J Reese
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Siru Liu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Bryan Steitz
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Allison McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Elise Russo
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Brian Koh
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jessica Ancker
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| |
Collapse
|
11
|
Lowery J, Fagerlin A, Larkin AR, Wiener RS, Skurla SE, Caverly TJ. Implementation of a Web-Based Tool for Shared Decision-making in Lung Cancer Screening: Mixed Methods Quality Improvement Evaluation. JMIR Hum Factors 2022; 9:e32399. [PMID: 35363144 PMCID: PMC9015752 DOI: 10.2196/32399] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 10/28/2021] [Accepted: 11/28/2021] [Indexed: 12/18/2022] Open
Abstract
Background Lung cancer risk and life expectancy vary substantially across patients eligible for low-dose computed tomography lung cancer screening (LCS), which has important consequences for optimizing LCS decisions for different patients. To account for this heterogeneity during decision-making, web-based decision support tools are needed to enable quick calculations and streamline the process of obtaining individualized information that more accurately informs patient-clinician LCS discussions. We created DecisionPrecision, a clinician-facing web-based decision support tool, to help tailor the LCS discussion to a patient’s individualized lung cancer risk and estimated net benefit. Objective The objective of our study is to test two strategies for implementing DecisionPrecision in primary care at eight Veterans Affairs medical centers: a quality improvement (QI) training approach and academic detailing (AD). Methods Phase 1 comprised a multisite, cluster randomized trial comparing the effectiveness of standard implementation (adding a link to DecisionPrecision in the electronic health record vs standard implementation plus the Learn, Engage, Act, and Process [LEAP] QI training program). The primary outcome measure was the use of DecisionPrecision at each site before versus after LEAP QI training. The second phase of the study examined the potential effectiveness of AD as an implementation strategy for DecisionPrecision at all 8 medical centers. Outcomes were assessed by comparing absolute tool use before and after AD visits and conducting semistructured interviews with a subset of primary care physicians (PCPs) following the AD visits. Results Phase 1 findings showed that sites that participated in the LEAP QI training program used DecisionPrecision significantly more often than the standard implementation sites (tool used 190.3, SD 174.8 times on average over 6 months at LEAP sites vs 3.5 SD 3.7 at standard sites; P<.001). However, this finding was confounded by the lack of screening coordinators at standard implementation sites. In phase 2, there was no difference in the 6-month tool use between before and after AD (95% CI −5.06 to 6.40; P=.82). Follow-up interviews with PCPs indicated that the AD strategy increased provider awareness and appreciation for the benefits of the tool. However, other priorities and limited time prevented PCPs from using them during routine clinical visits. Conclusions The phase 1 findings did not provide conclusive evidence of the benefit of a QI training approach for implementing a decision support tool for LCS among PCPs. In addition, phase 2 findings showed that our light-touch, single-visit AD strategy did not increase tool use. To enable tool use by PCPs, prediction-based tools must be fully automated and integrated into electronic health records, thereby helping providers personalize LCS discussions among their many competing demands. PCPs also need more time to engage in shared decision-making discussions with their patients. Trial Registration ClinicalTrials.gov NCT02765412; https://clinicaltrials.gov/ct2/show/NCT02765412
Collapse
Affiliation(s)
- Julie Lowery
- Center for Clinical Management Research, Ann Arbor VA Healthcare System, Ann Arbor, MI, United States
| | - Angela Fagerlin
- Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT, United States
- Informatics Decision-Enhancement and Analytics Sciences Center for Innovation, VA Salt Lake City Healthcare System, Salt Lake City, MI, United States
| | - Angela R Larkin
- Center for Clinical Management Research, Ann Arbor VA Healthcare System, Ann Arbor, MI, United States
| | - Renda S Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA, United States
- The Pulmonary Center, Boston University School of Medicine, Boston, MA, United States
| | - Sarah E Skurla
- Center for Clinical Management Research, Ann Arbor VA Healthcare System, Ann Arbor, MI, United States
| | - Tanner J Caverly
- Center for Clinical Management Research, Ann Arbor VA Healthcare System, Ann Arbor, MI, United States
- Department of Learning Health Sciences, University of Michigan School of Medicine, Ann Arbor, MI, United States
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| |
Collapse
|
12
|
Kukhareva PV, Weir C, Fiol GD, Aarons GA, Taft TY, Schlechter CR, Reese TJ, Curran RL, Nanjo C, Borbolla D, Staes CJ, Morgan KL, Kramer HS, Stipelman CH, Shakib JH, Flynn MC, Kawamoto K. Evaluation in Life Cycle of Information Technology (ELICIT) framework: Supporting the innovation life cycle from business case assessment to summative evaluation. J Biomed Inform 2022; 127:104014. [PMID: 35167977 PMCID: PMC8959015 DOI: 10.1016/j.jbi.2022.104014] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/16/2021] [Accepted: 02/02/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Our objective was to develop an evaluation framework for electronic health record (EHR)-integrated innovations to support evaluation activities at each of four information technology (IT) life cycle phases: planning, development, implementation, and operation. METHODS The evaluation framework was developed based on a review of existing evaluation frameworks from health informatics and other domains (human factors engineering, software engineering, and social sciences); expert consensus; and real-world testing in multiple EHR-integrated innovation studies. RESULTS The resulting Evaluation in Life Cycle of IT (ELICIT) framework covers four IT life cycle phases and three measure levels (society, user, and IT). The ELICIT framework recommends 12 evaluation steps: (1) business case assessment; (2) stakeholder requirements gathering; (3) technical requirements gathering; (4) technical acceptability assessment; (5) user acceptability assessment; (6) social acceptability assessment; (7) social implementation assessment; (8) initial user satisfaction assessment; (9) technical implementation assessment; (10) technical portability assessment; (11) long-term user satisfaction assessment; and (12) social outcomes assessment. DISCUSSION Effective evaluation requires a shared understanding and collaboration across disciplines throughout the entire IT life cycle. In contrast with previous evaluation frameworks, the ELICIT framework focuses on all phases of the IT life cycle across the society, user, and IT levels. Institutions seeking to establish evaluation programs for EHR-integrated innovations could use our framework to create such shared understanding and justify the need to invest in evaluation. CONCLUSION As health care undergoes a digital transformation, it will be critical for EHR-integrated innovations to be systematically evaluated. The ELICIT framework can facilitate these evaluations.
Collapse
Affiliation(s)
- Polina V. Kukhareva
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Gregory A. Aarons
- Department of Psychiatry, UC San Diego ACTRI Dissemination and Implementation Science Center, UC San Diego, La Jolla, CA, USA
| | - Teresa Y. Taft
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Chelsey R. Schlechter
- Department of Population Health Sciences, Center for Health Outcomes and Population Equity, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Thomas J. Reese
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Rebecca L. Curran
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Claude Nanjo
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
| | - Damian Borbolla
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
| | | | - Keaton L. Morgan
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Heidi S. Kramer
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | | | - Julie H. Shakib
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Michael C. Flynn
- Department of Family & Preventive Medicine, University of Utah, Salt Lake City, UT, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
| |
Collapse
|