EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect

Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients' health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70-72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Level - Adult (CARLA) baseline score was most significant in predicting outcome over time (odds ratio 4.1 + 2.27). Other variables with consistently significant impact on outcome included payer, diagnostic category, location and provision of case management services. Conclusions: This approach represents a promising avenue toward reducing the current gap between research and practice across healthcare, developing data-driven clinical decision support based on real-world populations, and serving as a component of embedded clinical artificial intelligences that "learn" over time.Comment: Keywords: Data Mining; Decision Support Systems, Clinical; Electronic Health Records; Implementation; Evidence-Based Medicine; Data Warehouse; (2012). EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect. Health Policy and Technology. arXiv admin note: substantial text overlap with arXiv:1112.166

Using an Implementation Research Framework to Identify Potential Facilitators and Barriers of an Intervention to Increase HPV Vaccine Uptake

Background: Although the incidence of cervical cancer has been decreasing in the United States over the last decade, Hispanic and African American women have substantially higher rates than Caucasian women. The human papillomavirus (HPV) is a necessary, although insufficient, cause of cervical cancer. In the United States in 2013, only 37.6% of girls 13 to 17 years of age received the recommended 3 doses of a vaccine that is almost 100% efficacious for preventing infection with viruses that are responsible for 70% of cervical cancers. Implementation research has been underutilized in interventions for increasing vaccine uptake. The Consolidated Framework for Implementation Research (CFIR), an approach for designing effective implementation strategies, integrates 5 domains that may include barriers and facilitators of HPV vaccination. These include the innovative practice (Intervention), communities where youth and parents live (Outer Setting), agencies offering vaccination (Inner Setting), health care staff (Providers), and planned execution and evaluation of intervention delivery (Implementation Process). Methods: Secondary qualitative analysis of transcripts of interviews with 30 community health care providers was conducted using the CFIR to code potential barriers and facilitators of HPV vaccination implementation. Results: All CFIR domains except Implementation Process were well represented in providers\u27 statements about challenges and supports for HPV vaccination. Conclusion: A comprehensive implementation framework for promoting HPV vaccination may increase vaccination rates in ethnically diverse communities. This study suggests that the CFIR can be used to guide clinicians in planning implementation of new approaches to increasing HPV vaccine uptake in their settings. Further research is needed to determine whether identifying implementation barriers and facilitators in all 5 CFIR domains as part of developing an intervention contributes to improved HPV vaccination rates

Psychosocial concerns and needs of cancer survivors treated at a comprehensive cancer center and a community safety net hospital

The number of cancer survivors in the United States is expected to grow to 18 million by 2020 because of improved cancer treatment outcomes and the aging of the population.[1] Many cancer survivors are at increased risk for cancer recurrence and other adverse long-term physical and psychosocial conditions.[2-5] Disparities in survival are associated with inadequate or no health insurance coverage because individuals are more likely to be diagnosed with cancer at later stages,[6] and higher incidence for some cancers among African Americans.[7] Few studies have examined psychosocial health disparities during cancer survivorship,[8-13] and little is known about how psychosocial factors subsequent to diagnosis affect survival and long-term outcomes. [4,14] While clinical care relevant to survivorship outcomes is advancing, [15, 16] optimal practices for preparing survivors for treatment and transitioning off treatment have yet to be defined. [11, 15, 17] Furthermore, guidance is needed for serving minority and underserved survivor populations where health disparities exist.[7] More data are needed about incidence of adverse outcomes and their determinants, overall and in disparity populations to inform development of best practices for preventive interventions. The purpose of this study was to identify similarities and differences among two groups of survivors in (1) sources of information at time of cancer diagnosis, (2) sources of support used during and after treatment, (3) stressors and challenges during and after treatment, and (4) coping strategies[18] used during and following cancer treatment. These factors might be associated with health services use,[19] and with survivorship disparities.[20] One group was treated at Vanderbilt-Ingram Cancer Center (VICC), an NCI-designated comprehensive cancer center, and the other at Meharry Medical College (MMC), its partner medical setting that serves patients who are mostly publicly-insured and uninsured. Secondary analysis of data from focus group participants was undertaken to address the four study topics and to guide future development of interventions tailored to preferences and needs of diverse survivors

Improving Community Advisory Board Engagement In Precision Medicine Research To Reduce Health Disparities

Community Advisory Boards (CABs) are used in efforts to reduce health disparities; however, there is little documentation in the literature regarding their use in precision medicine research. In this case study, an academic-CAB partnership developed a questionnaire and patient educational materials for two precision smoking cessation interventions that involved use of genetic information. The community-engaged research (CEnR) literature provided a framework for enhancing benefits to CAB members involved in developing research documents for use with a low-income, ethnically diverse population of smokers. The academic partners integrated three CEnR strategies: 1) in-meeting statements acknowledging their desire to learn from community partners, 2) in-meeting written feedback to and from community partners, and 3) a survey to obtain CAB member feedback post-meetings. Strategies 1 and 2 yielded modifications to pertinent study materials, as well as suggestions for improving meeting operations that were then adopted, as appropriate, by the academic partners. The survey indicated that CAB members valued the meeting procedure changes which appeared to have contributed to improvements in attendance and satisfaction with the meetings. Further operationalization of relevant partnership constructs and development of tools for measuring these aspects of community-academic partnerships is warranted to support community engagement in precision medicine research studies

Time from Screening Mammography to Biopsy and from Biopsy to Breast Cancer Treatment among Black and White, Women Medicare Beneficiaries Not Participating in a Health Maintenance Organization

Purpose There is a breast cancer mortality gap adversely affecting Black women in the United States. This study assessed the relationship between number of days between abnormal mammogram, biopsy, and treatment among Medicare (Part B) beneficiaries ages 65 to 74 and 75 to 84 years, accounting for race and comorbidity. Methods A cohort of non-Hispanic Black and non-Hispanic White women residing in the continental United States and receiving no services from a health maintenance organization was randomly selected from the Center for Medicare and Medicaid Services denominator file. The cohort was followed from 2005 to 2008 using Center for Medicare and Medicaid Services claims data. The sample included 4,476 women (weighted n = 70,731) with a diagnosis of breast cancer. Cox proportional hazard modeling was used to identify predictors of waiting times. Findings Black women had a mean of 16.7 more days between biopsy and treatment (p \u3c .001) and 15.7 more days from mammogram to treatment (p = .003) than White women. Median duration from abnormal mammogram to treatment exceeded National Quality Measures for Breast Centers medians regardless of race, age, or number of comorbidities (overall 43 days vs. the National Quality Measures for Breast Centers value of 28 days). Conclusions Medical care delays may contribute, in part, to the widening breast cancer mortality gap between Black women and White women. Further study, with additional clinical and social information, is needed to broaden scientific understanding of racial determinants and assess the clinical significance of mammogram to treatment times among Medicare beneficiaries

Attitudes toward Precision Treatment of Smoking in the Southern Community Cohort Study

Background: Precision interventions using biological data may enhance smoking treatment, yet are understudied among smokers who are disproportionately burdened by smoking-related disease. Methods: We surveyed smokers in the NCI-sponsored Southern Community Cohort Study, consisting primarily of African-American, low-income adults. Seven items assessed attitudes toward aspects of precision smoking treatment, from undergoing tests to acting on results. Items were dichotomized as favorable (5 = strongly agree/4 = agree) versus less favorable (1 = strongly disagree/2 = disagree/3 = neutral); a summary score reflecting generalized attitudes was also computed. Multivariable logistic regression tested independent associations of motivation (precontemplation, contemplation, and preparation) and confidence in quitting (low, medium, and high) with generalized attitudes, controlling for sociodemographic factors and nicotine dependence. Results: More than 70% of respondents endorsed favorable generalized attitudes toward precision medicine, with individual item favorability ranging from 64% to 83%. Smokers holding favorable generalized attitudes reported higher income and education (P \u3c 0.05). Predicted probabilities of favorable generalized attitudes ranged from 63% to 75% across motivation levels [contemplation vs. precontemplation: adjusted odds ratio (AOR) = 2.10, 95% confidence interval (CI), 1.36–3.25, P = 0.001; preparation vs. precontemplation: AOR = 1.83, 95% CI, 1.20–2.78, P = 0.005; contemplation vs. preparation: AOR = 1.15, 95% CI, 0.75–1.77, P = 0.52] and from 59% to 78% across confidence (medium vs. low: AOR = 1.91, 95% CI, 1.19–3.07, P = 0.007; high vs. low: AOR = 2.62, 95% CI, 1.68–4.10, P \u3c 0.001; medium vs. high: AOR = 0.73, 95% CI, 0.48–1.11, P = 0.14). Conclusions: Among disproportionately burdened community smokers, most hold favorable attitudes toward precision smoking treatment. Individuals with lower motivation and confidence to quit may benefit from additional intervention to engage with precision smoking treatment. Impact: Predominantly favorable attitudes toward precision smoking treatment suggest promise for future research testing their effectiveness and implementation

Characteristics of Parents Involved in a Parent Child Center-Head Start Program

An exploratory study was conducted to provide information for the coordinators of the Parent Child Center and Head Start (PCC-HS) Program in Leitchfield, Kentucky. Staff concern for parent involvement led to a search for parent characteristics which correlate with amount of participation in program activities. Sixty-five families, which included 65 mothers, 47 fathers, and 121 children, constituted the study sample. Data were obtained from records maintained by the PCC-HS staff. Variables included number of hours volunteered, age and level of education of each parent, estimated family income, family size, mother\u27s enrollment in PCC when pregnant, number of children in the family who have been enrolled in PCC and HS, presence of a handicapped child in the family, source of referral, and length of contact with the program. Hypotheses were that greater parent involvement was associated with presence of a handicapped child in the family, enrollment in PCC, parental initiation of contact with the staff, and longer contact with the staff. Parent involvement was defined as the number of volunteer hours recorded by each parent during an eight month period. Relationships between involvement and the remaining variables were assessed with a Kruskal-Wallis one-way analysis of variance. Only age of father and presence of a handicapped child in the family were significantly related to the number of hours volunteered

Characteristics of Parents Involved in a Parent Child Center-Head Start Program

An exploratory study was conducted to provide information for the coordinators of the Parent Child Center and Head Start (PCC-HS) Program in Leitchfield, Kentucky. Staff concern for parent involvement led to a search for parent characteristics which correlate with amount of participation in program activities. Sixty-five families, which included 65 mothers, 47 fathers, and 121 children, constituted the study sample. Data were obtained from records maintained by the PCC-HS staff. Variables included number of hours volunteered, age and level of education of each parent, estimated family income, family size, mother\u27s enrollment in PCC when pregnant, number of children in the family who have been enrolled in PCC and HS, presence of a handicapped child in the family, source of referral, and length of contact with the program. Hypotheses were that greater parent involvement was associated with presence of a handicapped child in the family, enrollment in PCC, parental initiation of contact with the staff, and longer contact with the staff. Parent involvement was defined as the number of volunteer hours recorded by each parent during an eight month period. Relationships between involvement and the remaining variables were assessed with a Kruskal-Wallis one-way analysis of variance. Only age of father and presence of a handicapped child in the family were significantly related to the number of hours volunteered

EHRs connect research and practice: Where predictive modeling, artificial intelligence, and clinical decision support intersect

Objectives Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data—the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients’ health conditions and responses to treatment over time. Methods We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70% to 72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Level—Adult (CARLA) baseline score was most significant in predicting outcome over time (odds ratio 4.1+2.27). Other variables with consistently significant impact on outcome included payer, diagnostic category, location and provision of case management services. Conclusions This approach represents a promising avenue toward reducing the current gap between research and practice across healthcare, developing data-driven clinical decision support based on real-world populations, and serving as a component of embedded clinical artificial intelligences that “learn” over time. Research highlights ► EHRs are increasingly likely to contain data and functionality that can support computational approaches to healthcare. ► Predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response at baseline. ► Clinical decision support can be conceptualized as a form of artificial intelligence embedded within clinical systems. ► Despite challenges, data-driven clinical decision support based on real-world populations offers numerous advantages. ► Such approaches may also contribute to better implementation of research into real-world clinical practice

Use of implementation science in tobacco control intervention studies in the USA between 2000 and 2020: a scoping review protocol

Introduction Despite continuing efforts to reduce tobacco use in the USA, decline in smoking rates have stalled and smoking remains a major contributor to preventable death. Implementation science could potentially improve uptake and impact of evidence-based tobacco control interventions; however, no previous studies have systematically examined how implementation science has been used in this field. Our scoping review will describe the use of implementation science in tobacco control in the USA, identify relevant gaps in research and suggest future directions for implementation science application to tobacco control.Methods and analysis Our team, including a medical research librarian, will conduct a scoping review guided primarily by Arksey and O’Malley’s methodology. We will search English language peer-reviewed literature published from 2000 to 31 December 2020 for terms synonymous with ‘tobacco use’, ‘prevention’, ‘cessation’ and ‘implementation science’. The databases included in this search are MEDLINE (PubMed), Embase (Ovid), CINAHL (EBSCOhost), PsycINFO (ProQuest), ERIC (ProQuest) and the Cochrane Library (Wiley). We will include cohort and quasi-experimental studies, single-group experiments and randomised trials that report qualitative and/or quantitative data related to applying implementation science to the planning and/or delivery of interventions to prevent or decrease the use of tobacco products. Studies must target potential or active tobacco users, intervention providers such as educators or healthcare professionals, or US policy-makers. A minimum of two reviewers will independently examine each title and abstract for relevance, and each eligible full text for inclusion and analysis. Use of implementation science, demonstrated by explicit reference to implementation frameworks, strategies or outcomes, will be extracted from included studies and summarised.Ethics and dissemination This study is exempt from ethics board approval. We will document the equity-orientation of included studies with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Equity Extension checklist. Results will be submitted for conferences and peer-reviewed journals.Trial registration number Open Science Framework Registry (6YRK8)