Using computational modeling to assess the impact of clinical decision support on cancer screening improvement strategies within the community health centers

Adoption of clinical decision support (CDS) systems leads to improved clinical performance through improved clinician decision making, adherence to evidence-based guidelines, medical error reduction, and more efficient information transfer and to reduction in health care disparities in under-resourced settings. However, little information on CDS use in the community health care (CHC) setting exists. This study examines if organizational, provider, or patient level factors can successfully predict the level of CDS use in the CHC setting with regard to breast, cervical, and colorectal cancer screening. This study relied upon 37 summary measures obtained from the 2005 Cancer Health Disparities Collaborative (HDCC) national survey of 44 randomly selected community health centers. A multi-level framework was designed that employed an all-subsets linear regression to discover relationships between organizational/practice setting, provider, and patient characteristics and the outcome variable, a composite measure of community health center CDS intensity-of-use. Several organizational and provider level factors from our conceptual model were identified to be positively associated with CDS level of use in community health centers. The level of CDS use (e.g., computerized reminders, provider prompts at point-of-care) in support of breast, cervical, and colorectal cancer screening rate improvement in vulnerable populations is determined by both organizational/practice setting and provider factors. Such insights can better facilitate the increased uptake of CDS in CHCs that allows for improved patient tracking, disease management, and early detection in cancer prevention and control within vulnerable populations.

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Challenges involved in establishing a web-based clinical decision support tool in community health centers

Healthcare ◽

10.1016/j.hjdsi.2020.100488 ◽

2020 ◽

Vol 8 (4) ◽

pp. 100488

Author(s):

Rachel Gold ◽

Mary Middendorf ◽

John Heintzman ◽

Joan Nelson ◽

Patrick O'Connor ◽

...

Keyword(s):

Decision Support ◽

Community Health ◽

Clinical Decision Support ◽

Community Health Centers ◽

Decision Support Tool ◽

Clinical Decision ◽

Health Centers ◽

Web Based ◽

Support Tool ◽

Clinical Decision Support Tool

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Using EHR-Based Clinical Decision Support to Provide Social Risk-Informed Care in Community Health Centers: Study Protocol and Design (Preprint)

JMIR Research Protocols ◽

10.2196/31733 ◽

2021 ◽

Author(s):

Rachel Gold ◽

Christina Sheppler ◽

Danielle Hessler ◽

Arwen Bunce ◽

Erika Cottrell ◽

...

Keyword(s):

Decision Support ◽

Community Health ◽

Clinical Decision Support ◽

Study Protocol ◽

Community Health Centers ◽

Clinical Decision ◽

Health Centers ◽

Social Risk

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Organizational Factors Influencing the Use of Clinical Decision Support for Improving Cancer Screening Within Community Health Centers

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.2014010101 ◽

2014 ◽

Vol 9 (1) ◽

pp. 1-29 ◽

Cited By ~ 1

Author(s):

Timothy Jay Carney ◽

Michael Weaver ◽

Anna M. McDaniel ◽

Josette Jones ◽

David A. Haggstrom

Keyword(s):

Colorectal Cancer ◽

Health Care ◽

Decision Support ◽

Cancer Screening ◽

Community Health ◽

Community Health Centers ◽

Clinical Decision ◽

Health Centers ◽

Organizational Practice ◽

Practice Setting

Adoption of clinical decision support (CDS) systems leads to improved clinical performance through improved clinician decision making, adherence to evidence-based guidelines, medical error reduction, and more efficient information transfer and to reduction in health care disparities in under-resourced settings. However, little information on CDS use in the community health care (CHC) setting exists. This study examines if organizational, provider, or patient level factors can successfully predict the level of CDS use in the CHC setting with regard to breast, cervical, and colorectal cancer screening. This study relied upon 37 summary measures obtained from the 2005 Cancer Health Disparities Collaborative (HDCC) national survey of 44 randomly selected community health centers. A multi-level framework was designed that employed an all-subsets linear regression to discover relationships between organizational/practice setting, provider, and patient characteristics and the outcome variable, a composite measure of community health center CDS intensity-of-use. Several organizational and provider level factors from our conceptual model were identified to be positively associated with CDS level of use in community health centers. The level of CDS use (e.g., computerized reminders, provider prompts at point-of-care) in support of breast, cervical, and colorectal cancer screening rate improvement in vulnerable populations is determined by both organizational/practice setting and provider factors. Such insights can better facilitate the increased uptake of CDS in CHCs that allows for improved patient tracking, disease management, and early detection in cancer prevention and control within vulnerable populations.

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Using EHR-Based Clinical Decision Support to Provide Social Risk-Informed Care in Community Health Centers: Study Protocol and Design (Preprint)

10.2196/preprints.31733 ◽

2021 ◽

Author(s):

Rachel Gold ◽

Christina Sheppler ◽

Danielle Hessler ◽

Arwen Bunce ◽

Erika Cottrell ◽

...

Keyword(s):

Decision Support ◽

Health Outcomes ◽

Clinical Decision Support ◽

Community Health Centers ◽

Intervention Group ◽

Clinical Decision ◽

Health Centers ◽

Social Risk ◽

Social Risks ◽

Improve Health

BACKGROUND Consistent and compelling evidence demonstrates that social and economic adversity impact health outcomes. In response, many healthcare professional organizations recommend screening patients for experiences of social and economic adversity or ‘social risks’—e.g., food, housing, and transportation insecurity—in the context of care. The guidance on how healthcare providers can act on documented social risk data to improve health outcomes is nascent. One strategy recommended by the National Academy of Medicine involves using social risk data to adapt care plans in ways that accommodate patients’ social risks. OBJECTIVE This study’s aims are to (1) develop electronic health record-based clinical decision support (CDS) tools that suggest social risk-informed care plan adaptations for patients with diabetes and/or hypertension; (2) assess tool adoption and its impact on selected Clinical Quality Measures in community health centers; and (3) examine perceptions of tool usability and impact on care quality. METHODS A systematic scoping review and several stakeholder activities will be conducted to inform development of the CDS tools. The tools will be pilot tested to obtain user input, and their content and form revised based on this input. A randomized quasi-experimental design will then be used to assess the revised tools’ impact. Eligible clinics will be randomized to a control group or potential intervention group; clinics will be recruited from the potential intervention group in a random order until six are enrolled in the study. Intervention clinics will have access to the CDS tools in their EHR, will receive minimal implementation support, and will be followed for 18 months to evaluate tool adoption and the impact of tool use on patient blood pressure and glucose control. RESULTS This study was funded in January 2020 by the National Institute on Minority Health and Health Disparities of the National Institutes of Health. Formative activities will take place from April 2020-July 2021; the CDS tools will be developed May 2021-November 2022; the pilot study will be conducted August 2021-July 2022; and the main trial will occur December 2022-May 2024. Study data will be analyzed, and results disseminated, in 2024. CONCLUSIONS Patients’ social risk information must be presented to care teams in a way that facilitates social risk-informed care. To our knowledge, this study is the first to develop and test EHR-embedded CDS tools designed to support the provision of social risk-informed care. Study results will add needed understanding of how to use social risk data to improve health outcomes and reduce disparities.

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The impact of personalized clinical decision support on primary care patients’ views of cancer prevention and screening: a cross-sectional survey

BMC Health Services Research ◽

10.1186/s12913-021-06551-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Daniel M. Saman ◽

Ella A. Chrenka ◽

Melissa L. Harry ◽

Clayton I. Allen ◽

Laura A. Freitag ◽

...

Keyword(s):

Primary Care ◽

Decision Support ◽

Cancer Screening ◽

Cancer Prevention ◽

Weight Management ◽

Clinical Decision Support ◽

Clinical Decision ◽

Cross Sectional Survey ◽

Cross Sectional ◽

The Impact

Abstract Background Few studies have assessed the impact of clinical decision support (CDS), with or without shared decision-making tools (SDMTs), on patients’ perceptions of cancer screening or prevention in primary care settings. This cross-sectional survey was conducted to understand primary care patient’s perceptions on cancer screening or prevention. Methods We mailed surveys (10/2018–1/2019) to 749 patients aged 18 to 75 years within 15 days after an index clinical encounter at 36 primary care clinics participating in a clinic-randomized control trial of a CDS system for cancer prevention. All patients were overdue for cancer screening or human papillomavirus vaccination. The survey compared respondents’ answers by study arm: usual care; CDS; or CDS + SDMT. Results Of 387 respondents (52% response rate), 73% reported having enough time to discuss cancer prevention options with their primary care provider (PCP), 64% reported their PCP explained the benefits of the cancer screening choice very well, and 32% of obese patients reported discussing weight management, with two-thirds reporting selecting a weight management intervention. Usual care respondents were significantly more likely to decide on colorectal cancer screening than CDS respondents (p < 0.01), and on tobacco cessation than CDS + SDMT respondents (p = 0.02) and both CDS and CDS + SDMT respondents (p < 0.001). Conclusions Most patients reported discussing cancer prevention needs with PCPs, with few significant differences between the three study arms in patient-reported cancer prevention care. Upcoming research will assess differences in screening and vaccination rates between study arms during the post-intervention follow-up period. Trial registration clinicaltrials.gov, NCT02986230, December 6, 2016.

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A Narrative Review of Clinical Decision Support for Inpatient Clinical Pharmacists

Applied Clinical Informatics ◽

10.1055/s-0041-1722916 ◽

2021 ◽

Vol 12 (02) ◽

pp. 199-207

Author(s):

Liang Yan ◽

Thomas Reese ◽

Scott D. Nelson

Keyword(s):

Patient Safety ◽

Quality Of Care ◽

Decision Support ◽

Clinical Decision Support ◽

Clinical Decision ◽

Controlled Trials ◽

Clinical Pharmacists ◽

Randomized Controlled ◽

The Impact

Abstract Objective Increasingly, pharmacists provide team-based care that impacts patient care; however, the extent of recent clinical decision support (CDS), targeted to support the evolving roles of pharmacists, is unknown. Our objective was to evaluate the literature to understand the impact of clinical pharmacists using CDS. Methods We searched MEDLINE, EMBASE, and Cochrane Central for randomized controlled trials, nonrandomized trials, and quasi-experimental studies which evaluated CDS tools that were developed for inpatient pharmacists as a target user. The primary outcome of our analysis was the impact of CDS on patient safety, quality use of medication, and quality of care. Outcomes were scored as positive, negative, or neutral. The secondary outcome was the proportion of CDS developed for tasks other than medication order verification. Study quality was assessed using the Newcastle–Ottawa Scale. Results Of 4,365 potentially relevant articles, 15 were included. Five studies were randomized controlled trials. All included studies were rated as good quality. Of the studies evaluating inpatient pharmacists using a CDS tool, four showed significantly improved quality use of medications, four showed significantly improved patient safety, and three showed significantly improved quality of care. Six studies (40%) supported expanded roles of clinical pharmacists. Conclusion These results suggest that CDS can support clinical inpatient pharmacists in preventing medication errors and optimizing pharmacotherapy. Moreover, an increasing number of CDS tools have been developed for pharmacists' roles outside of order verification, whereby further supporting and establishing pharmacists as leaders in safe and effective pharmacotherapy.

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Clinical Decision Support for Ovarian Carcinoma Subtype Classification: A Pilot Observer Study With Pathology Trainees

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2019-0390-oa ◽

2019 ◽

Vol 144 (7) ◽

pp. 869-877 ◽

Cited By ~ 1

Author(s):

Marios A. Gavrielides ◽

Meghan Miller ◽

Ian S. Hagemann ◽

Heba Abdelal ◽

Zahra Alipour ◽

...

Keyword(s):

Decision Support ◽

Ovarian Carcinoma ◽

Clinical Decision Support ◽

Clinical Decision ◽

Subspecialty Training ◽

Reference Standard ◽

Observer Study ◽

Subtype Classification ◽

The Impact ◽

Whole Slide Images

Context.— Clinical decision support (CDS) systems could assist less experienced pathologists with certain diagnostic tasks for which subspecialty training or extensive experience is typically needed. The effect of decision support on pathologist performance for such diagnostic tasks has not been examined. Objective.— To examine the impact of a CDS tool for the classification of ovarian carcinoma subtypes by pathology trainees in a pilot observer study using digital pathology. Design.— Histologic review on 90 whole slide images from 75 ovarian cancer patients was conducted by 6 pathology residents using: (1) unaided review of whole slide images, and (2) aided review, where in addition to whole slide images observers used a CDS tool that provided information about the presence of 8 histologic features important for subtype classification that were identified previously by an expert in gynecologic pathology. The reference standard of ovarian subtype consisted of majority consensus from a panel of 3 gynecologic pathology experts. Results.— Aided review improved pairwise concordance with the reference standard for 5 of 6 observers by 3.3% to 17.8% (for 2 observers, increase was statistically significant) and mean interobserver agreement by 9.2% (not statistically significant). Observers benefited the most when the CDS tool prompted them to look for missed histologic features that were definitive for a certain subtype. Observer performance varied widely across cases with unanimous and nonunanimous reference classification, supporting the need for balancing data sets in terms of case difficulty. Conclusions.— Findings showed the potential of CDS systems to close the knowledge gap between pathologists for complex diagnostic tasks.

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