Pre-implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare system
Abstract Background Cancer is a leading cause of death in the United States. Primary care providers (PCPs) juggle patient cancer prevention and screening along with managing acute and chronic health problems. However, clinical decision support (CDS) may assist PCPs in addressing patients’ cancer pre...
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| Format: | Article |
| Language: | English |
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BMC
2020-06-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12911-020-01136-8 |
| _version_ | 1818995723558977536 |
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| author | Melissa L. Harry Daniel M. Saman Anjali R. Truitt Clayton I. Allen Kayla M. Walton Patrick J. O’Connor Heidi L. Ekstrom JoAnn M. Sperl-Hillen Joseph A. Bianco Thomas E. Elliott |
| author_facet | Melissa L. Harry Daniel M. Saman Anjali R. Truitt Clayton I. Allen Kayla M. Walton Patrick J. O’Connor Heidi L. Ekstrom JoAnn M. Sperl-Hillen Joseph A. Bianco Thomas E. Elliott |
| author_sort | Melissa L. Harry |
| collection | DOAJ |
| description | Abstract Background Cancer is a leading cause of death in the United States. Primary care providers (PCPs) juggle patient cancer prevention and screening along with managing acute and chronic health problems. However, clinical decision support (CDS) may assist PCPs in addressing patients’ cancer prevention and screening needs during short clinic visits. In this paper, we describe pre-implementation study design and cancer screening and prevention CDS changes made to maximize utilization and better fit a healthcare system’s goals and culture. We employed the Consolidated Framework for Implementation Research (CFIR), useful for evaluating the implementation of CDS interventions in primary care settings, in understanding barriers and facilitators that led to those changes. Methods In a three-arm, pragmatic, 36 clinic cluster-randomized control trial, we integrated cancer screening and prevention CDS and shared decision-making tools (SDMT) into an existing electronic medical record-linked cardiovascular risk management CDS system. The integrated CDS is currently being tested within a predominately rural upper Midwestern healthcare system. Prior to CDS implementation, we catalogued pre-implementation changes made from 2016 to 2018 based on: pre-implementation site engagement; key informant interviews with healthcare system rooming staff, providers, and leadership; and pilot testing. We identified influential barriers, facilitators, and changes made in response through qualitative content analysis of meeting minutes and supportive documents. We then coded pre-implementation changes made and associated barriers and facilitators using the CFIR. Results Based on our findings from system-wide pre-implementation engagement, pilot testing, and key informant interviews, we made changes to accommodate the needs of the healthcare system based on barriers and facilitators that fell within the Intervention Characteristics, Inner Setting, and Outer Setting CFIR domains. Changes included replacing the expansion of medical assistant roles in one intervention arm with targeted SDMT, as well as altering cancer prevention CDS and study design elements. Conclusions Pre-implementation changes to CDS may help meet healthcare systems’ evolving needs and optimize the intervention by being responsive to real-world implementation barriers and facilitators. Frameworks like the CFIR are useful tools for identifying areas where pre-implementation barriers and facilitators may result in design changes, both to research studies and CDS systems. Trial registration NCT02986230 . |
| first_indexed | 2024-12-20T21:18:23Z |
| format | Article |
| id | doaj.art-4e54a8c904d94970b98fc0d44b7cccad |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-20T21:18:23Z |
| publishDate | 2020-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-4e54a8c904d94970b98fc0d44b7cccad2022-12-21T19:26:21ZengBMCBMC Medical Informatics and Decision Making1472-69472020-06-0120111510.1186/s12911-020-01136-8Pre-implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare systemMelissa L. Harry0Daniel M. Saman1Anjali R. Truitt2Clayton I. Allen3Kayla M. Walton4Patrick J. O’Connor5Heidi L. Ekstrom6JoAnn M. Sperl-Hillen7Joseph A. Bianco8Thomas E. Elliott9Essentia Health, Essentia Institute of Rural HealthEssentia Health, Essentia Institute of Rural HealthHealthPartners InstituteEssentia Health, Essentia Institute of Rural HealthEssentia Health, Essentia Institute of Rural HealthHealthPartners InstituteHealthPartners InstituteHealthPartners InstituteEssentia Health – Ely ClinicHealthPartners InstituteAbstract Background Cancer is a leading cause of death in the United States. Primary care providers (PCPs) juggle patient cancer prevention and screening along with managing acute and chronic health problems. However, clinical decision support (CDS) may assist PCPs in addressing patients’ cancer prevention and screening needs during short clinic visits. In this paper, we describe pre-implementation study design and cancer screening and prevention CDS changes made to maximize utilization and better fit a healthcare system’s goals and culture. We employed the Consolidated Framework for Implementation Research (CFIR), useful for evaluating the implementation of CDS interventions in primary care settings, in understanding barriers and facilitators that led to those changes. Methods In a three-arm, pragmatic, 36 clinic cluster-randomized control trial, we integrated cancer screening and prevention CDS and shared decision-making tools (SDMT) into an existing electronic medical record-linked cardiovascular risk management CDS system. The integrated CDS is currently being tested within a predominately rural upper Midwestern healthcare system. Prior to CDS implementation, we catalogued pre-implementation changes made from 2016 to 2018 based on: pre-implementation site engagement; key informant interviews with healthcare system rooming staff, providers, and leadership; and pilot testing. We identified influential barriers, facilitators, and changes made in response through qualitative content analysis of meeting minutes and supportive documents. We then coded pre-implementation changes made and associated barriers and facilitators using the CFIR. Results Based on our findings from system-wide pre-implementation engagement, pilot testing, and key informant interviews, we made changes to accommodate the needs of the healthcare system based on barriers and facilitators that fell within the Intervention Characteristics, Inner Setting, and Outer Setting CFIR domains. Changes included replacing the expansion of medical assistant roles in one intervention arm with targeted SDMT, as well as altering cancer prevention CDS and study design elements. Conclusions Pre-implementation changes to CDS may help meet healthcare systems’ evolving needs and optimize the intervention by being responsive to real-world implementation barriers and facilitators. Frameworks like the CFIR are useful tools for identifying areas where pre-implementation barriers and facilitators may result in design changes, both to research studies and CDS systems. Trial registration NCT02986230 .http://link.springer.com/article/10.1186/s12911-020-01136-8Cancer prevention and screeningClinical decision supportConsolidated Framework for Implementation ResearchPre-implementation adaptationPrimary careShared decision-making tools |
| spellingShingle | Melissa L. Harry Daniel M. Saman Anjali R. Truitt Clayton I. Allen Kayla M. Walton Patrick J. O’Connor Heidi L. Ekstrom JoAnn M. Sperl-Hillen Joseph A. Bianco Thomas E. Elliott Pre-implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare system BMC Medical Informatics and Decision Making Cancer prevention and screening Clinical decision support Consolidated Framework for Implementation Research Pre-implementation adaptation Primary care Shared decision-making tools |
| title | Pre-implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare system |
| title_full | Pre-implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare system |
| title_fullStr | Pre-implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare system |
| title_full_unstemmed | Pre-implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare system |
| title_short | Pre-implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare system |
| title_sort | pre implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare system |
| topic | Cancer prevention and screening Clinical decision support Consolidated Framework for Implementation Research Pre-implementation adaptation Primary care Shared decision-making tools |
| url | http://link.springer.com/article/10.1186/s12911-020-01136-8 |
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