Special Issue on Informatics Education A Longitudinal Graduate Medical Education Curriculum in Clinical Informatics: Function, Structure, and Evaluation.

BACKGROUND: There is a need to integrate informatics education into medical training programs given the rise in demand for health informaticians and the call on the Accreditation Council for Graduate Medical Education (ACGME) and the body of undergraduate medical education (UGME) for implementation of informatics curricula. OBJECTIVES: This report outlines a 2-year longitudinal informatics curriculum now currently in its seventh year of implementation. This report is intended to inform United States (US) Graduate Medical Education (GME) program leaders of the necessary requirements for implementation of a similar program at their institution. METHODS: The curriculum aligns with the core content for the subspecialty of clinical informatics (CI) and is led by a multidisciplinary team with both informatics and clinical expertise. This educational pathway has a low direct cost and is a practical example of the academic learning health system (aLHS) in action. The pathway is housed within an internal medicine department at a large tertiary academic medical center. RESULTS: The curriculum has yielded 13 graduates from both internal medicine (11, 85%) and pediatrics (2, 15%) whose projects have spanned acute and ambulatory care and multiple specialties. Projects have included Clinical Decision Support (CDS) tools, of which some will be leveraged as substrate in applications seeking extramural funding. Graduates have gone on to CI board certification and fellowship, as well as several other specialties, creating a distributed network of clinicians with specialized experience in applied CI. CONCLUSIONS: An informatics curriculum at the GME level may increase matriculation to CI fellowship and more broadly increase development of the CI workforce through building a cadre of physicians with HIT expertise across specialties without formal CI board certification. We offer an example of a longitudinal pathway which is rooted in aLHS principles. The pathway requires a dedicated multidisciplinary team and departmental and IT leadership support.

Longitudinal Monitoring of Clinician-Patient Video Visits During the Peak of the COVID-19 Pandemic: Adoption and Sustained Challenges in an Integrated Health Care Delivery System.

BACKGROUND: Numerous prior opinion papers, administrative electronic health record data studies, and cross-sectional surveys of telehealth during the pandemic have been published, but none have combined assessments of video visit success monitoring with longitudinal assessments of perceived challenges to the rapid adoption of video visits during the pandemic. OBJECTIVE: This study aims to quantify (1) the use of video visits (compared with in-person and telephone visits) over time during the pandemic, (2) video visit successful connection rates, and (3) changes in perceived video visit challenges. METHODS: A web-based survey was developed for the dual purpose of monitoring and improving video visit implementation in our health care system during the COVID-19 pandemic. The survey included questions regarding rates of in-person, telephone, and video visits for clinician-patient encounters; the rate of successful connection for video visits; and perceived challenges to video visits (eg, software, hardware, bandwidth, and technology literacy). The survey was distributed via email to physicians, advanced practice professionals, and clinicians in May 2020. The survey was repeated in March 2021. Differences between the 2020 and 2021 responses were adjusted for within-respondent correlation across surveys and tested using generalized estimating equations. RESULTS: A total of 1126 surveys were completed (511 surveys in 2020 and 615 surveys in 2021). In 2020, only 21.7% (73/336) of clinicians reported no difficulty connecting with patients during video visits and 28.6% (93/325) of clinicians reported no difficulty in 2021. The distribution of the percentage of successfully connected video visits ("Over the past two weeks of scheduled visits, what percentage did you successfully connect with patients by video?") was not significantly different between 2020 and 2021 (P=.74). Challenges in conducting video visits persisted over time. Poor connectivity was the most common challenge reported by clinicians. This response increased over time, with 30.5% (156/511) selecting it as a challenge in 2020 and 37.1% (228/615) in 2021 (P=.01). Patients not having access to their electronic health record portals was also a commonly reported challenge (109/511, 21.3% in 2020 and 137/615, 22.3% in 2021, P=.73). CONCLUSIONS: During the pandemic, our health care delivery system rapidly adopted synchronous patient-clinician communication using video visits. As experience with video visits increased, the reported failure rate did not significantly decline, and clinicians continued to report challenges related to general network connectivity and patient access to technology.

The Academic Learning Health System: A Framework for Integrating the Multiple Missions of Academic Medical Centers.

The learning health system (LHS) has emerged over the past 15 years as a concept for improving health care delivery. Core aspects of the LHS concept include: promoting improved patient care through organizational learning, innovation, and continuous quality improvement; identifying, critically assessing, and translating knowledge and evidence into improved practices; building new knowledge and evidence around how to improve health care and health outcomes; analyzing clinical data to support learning, knowledge generation, and improved patient care; and engaging clinicians, patients, and other stakeholders in processes of learning, knowledge generation, and translation. However, the literature has paid less attention to how these LHS aspects may integrate with the multiple missions of academic medical centers (AMCs). The authors define an academic learning health system (aLHS) as an LHS built around a robust academic community and central academic mission, and they propose 6 features that emphasize how an aLHS differs from an LHS. An aLHS capitalizes on embedded academic expertise in health system sciences; engages the full spectrum of translational investigation from mechanistic basic sciences to population health; builds pipelines of experts in LHS sciences and clinicians with fluency in practicing in an LHS; applies core LHS principles to the development of curricula and clinical rotations for medical students, housestaff, and other learners; disseminates knowledge more broadly to advance the evidence for clinical practice and health systems science methods; and addresses social determinants of health, creating community partnerships to mitigate disparities and improve health equity. As AMCs evolve, the authors expect that additional differentiating features and ways to operationalize the aLHS will be identified and hope this article stimulates further discussion around the intersection of the LHS concept and AMCs.

Characteristics of Adult Primary Care Patients Who Use the Patient Portal: A Cross-Sectional Analysis.

BACKGROUND: The patient portal allows patients to engage with their health care team beyond the clinical encounter. While portals can improve patient outcomes, there may be disparities in which patients access the portal by sociodemographic factors. Understanding the characteristics of patients who use the portal could help design future interventions to expand portal adoption. OBJECTIVES: This study aimed to (1) examine the socioeconomic factors, comorbid conditions, and health care utilization among patients of a large academic primary care network who are users and non-users of the patient portal; and (2) describe the portal functions most frequently utilized. METHODS: We included all adult patients at Atrium Health Wake Forest Baptist who had at least two primary care visits between 2018 and 2019. Patients' demographics, comorbidities, health care utilization, and portal function usage were extracted from the electronic health record and merged with census data (income, education, and unemployment) from the American Community Survey. A myWakeHealth portal user was defined as a patient who used a bidirectional portal function at least once during the study period. We used multivariable logistic regression to determine which patient characteristics were independently associated with being a portal user. RESULTS: Of the 178,720 patients who met inclusion criteria, 32% (N = 57,122) were users of myWakeHealth. Compared to non-users, users were more likely to be 18 to 64 years of age, female, non-Hispanic White, married, commercially insured, have higher disease burden, and have lower health care utilization. Patients residing in areas with the highest educational attainment had 51% higher odds of being a portal user than the lowest (p <0.001). Among portal users, the most commonly used function was messaging clinic providers. CONCLUSION: We found that patient demographics and area socioeconomic factors were associated with patient portal adoption. These findings suggest that efforts to improve portal adoption should be targeted at vulnerable patients.

Pathways by Which Food Insecurity Is Associated With Atherosclerotic Cardiovascular Disease Risk.

Background Food insecurity (FI) has been associated with an increased atherosclerotic cardiovascular disease (ASCVD) risk; however, the pathways by which FI leads to worse cardiovascular health are unknown. We tested the hypothesis that FI is associated with ASCVD risk through nutritional/anthropometric (eg, worse diet quality and increased weight), psychological/mental health (eg, increased depressive symptoms and risk of substance abuse), and access to care pathways. Methods and Results We conducted a cross-sectional study of adults (aged 40-79 years) using the 2007 to 2016 National Health and Nutrition Examination Survey. Our primary exposure was household FI, and our outcome was 10-year ASCVD risk categorized as low (<5%), borderline (≥5% -<7.5%), intermediate (≥7.5%-<20%), and high risk (≥20%). We used structural equation modeling to evaluate the pathways and multiple mediation analysis to determine direct and indirect effects. Of the 12 429 participants, 2231 (18.0%) reported living in a food-insecure household; 5326 (42.9%) had a low ASCVD risk score, 1402 (11.3%) borderline, 3606 (29.0%) intermediate, and 2095 (16.9%) had a high-risk score. In structural models, we found significant path coefficients between FI and the nutrition/anthropometric (ß, 0.130; SE, 0.027; P<0.001), psychological/mental health (ß, 0.612; SE, 0.043; P<0.001), and access to care (ß, 0.110; SE, 0.036; P=0.002) pathways. We did not find a significant direct effect of FI on ASCVD risk, and the nutrition, psychological, and access to care pathways accounted for 31.6%, 43.9%, and 15.8% of the association, respectively. Conclusions We found that the association between FI and ASCVD risk category was mediated through the nutrition/anthropometric, psychological/mental health, and access to care pathways. Interventions that address all 3 pathways may be needed to mitigate the negative impact of FI on cardiovascular disease.

Implementing a Second-Level Observation Unit at a Large Academic Medical Center.

Supplemental digital content is available in the text.

Feasibility of Mobile Technology to Identify and Address Patients' Unmet Social Needs in a Primary Care Clinic.

Mobile health tools may overcome barriers to social needs screening; however, there are limited data on the feasibility of using these tools in clinical settings. The objective was to determine the feasibility of using a mobile health system to screen for patients' social needs. In one large primary care clinic, the authors tested a tablet-based system that screens patients for social needs, transmits results to the electronic health record, and alerts providers. All adult patients presenting for a nonurgent visit were eligible. The authors evaluated the feasibility of the system and conducted follow-up surveys to determine acceptability and if patients accessed resources through the process. All providers were surveyed. Of the 252 patients approached, 219 (86.9%) completed the screen. Forty-three (19.6%) required assistance with the tablet, and 150 (68.5%) screened positive for at least 1 unmet need (food, housing, or transportation). Of the 150, 103 (68.7%) completed a follow-up survey. The majority agreed that people would learn to use the tablet quickly. Forty-eight patients (46.6%) reported contacting at least 1 community organization through the process. Of the 27 providers, 23 (85.2%) completed a survey and >70% agreed the system would result in patients having better access to resources. It was feasible to use a tablet-based system to screen for social needs. Clinics considering using mobile tools will need to determine how to screen patients who may need assistance with the tool and how to connect patients to resources through the system based on the burden of unmet needs.

Primary care referrals to nephrology in patients with advanced kidney disease.

OBJECTIVES: Optimizing care for patients with advanced kidney disease requires close collaboration between primary care physicians (PCPs) and nephrologists. Factors associated with PCP referral to nephrology were assessed in patients with estimated glomerular filtration rates (eGFRs) less than 30 mL/min/1.73 m2. STUDY DESIGN: Electronic health record review at an integrated health care network. METHODS: Factors associated with referral status were identified using Fisher's exact tests, t tests, and multivariable logistic regression. RESULTS: Of 133,913 patients regularly seeing PCPs between October 2017 and September 2019, 1119 had a final eGFR less than 30 mL/min/1.73 m2 and were not on renal replacement therapy. Care was provided by 185 PCPs (61 practices). Analyses were restricted to the 97.1% (n = 1087) of patients who were African American or European American. Of these, 54.6% had not been referred to nephrology. Nonreferred patients had higher numbers of PCP visits (P = .004). In contrast, referred patients were younger, were more often African American, and had PCPs at the academic medical center (all P < .0001). Referred patients had more complex medical histories with higher Charlson Comorbidity Index scores, more hospitalizations, and greater numbers of inpatient days (all P < .0001). Analyses restricted to patients with serum creatinine concentration of at least 2 mg/dL yielded similar results. Age, number of hospitalizations, ancestry, academic physician, diabetic end-organ damage, peripheral vascular disease, and tumor status were independent predictors of nephrology referral. CONCLUSIONS: Impediments to appropriately timed nephrology referrals persist in patients with high likelihoods of progression to end-stage kidney disease. Improved access to nephrology care should be rapidly addressed to meet targets in the 2019 Executive Order on Advancing American Kidney Health.

Validation of an Electronic Health Record-Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems.

Importance: Suicide is a leading cause of mortality, with suicide-related deaths increasing in recent years. Automated methods for individualized risk prediction have great potential to address this growing public health threat. To facilitate their adoption, they must first be validated across diverse health care settings. Objective: To evaluate the generalizability and cross-site performance of a risk prediction method using readily available structured data from electronic health records in predicting incident suicide attempts across multiple, independent, US health care systems. Design, Setting, and Participants: For this prognostic study, data were extracted from longitudinal electronic health record data comprising International Classification of Diseases, Ninth Revision diagnoses, laboratory test results, procedures codes, and medications for more than 3.7 million patients from 5 independent health care systems participating in the Accessible Research Commons for Health network. Across sites, 6 to 17 years' worth of data were available, up to 2018. Outcomes were defined by International Classification of Diseases, Ninth Revision codes reflecting incident suicide attempts (with positive predictive value >0.70 according to expert clinician medical record review). Models were trained using naive Bayes classifiers in each of the 5 systems. Models were cross-validated in independent data sets at each site, and performance metrics were calculated. Data analysis was performed from November 2017 to August 2019. Main Outcomes and Measures: The primary outcome was suicide attempt as defined by a previously validated case definition using International Classification of Diseases, Ninth Revision codes. The accuracy and timeliness of the prediction were measured at each site. Results: Across the 5 health care systems, of the 3 714 105 patients (2 130 454 female [57.2%]) included in the analysis, 39 162 cases (1.1%) were identified. Predictive features varied by site but, as expected, the most common predictors reflected mental health conditions (eg, borderline personality disorder, with odds ratios of 8.1-12.9, and bipolar disorder, with odds ratios of 0.9-9.1) and substance use disorders (eg, drug withdrawal syndrome, with odds ratios of 7.0-12.9). Despite variation in geographical location, demographic characteristics, and population health characteristics, model performance was similar across sites, with areas under the curve ranging from 0.71 (95% CI, 0.70-0.72) to 0.76 (95% CI, 0.75-0.77). Across sites, at a specificity of 90%, the models detected a mean of 38% of cases a mean of 2.1 years in advance. Conclusions and Relevance: Across 5 diverse health care systems, a computationally efficient approach leveraging the full spectrum of structured electronic health record data was able to detect the risk of suicidal behavior in unselected patients. This approach could facilitate the development of clinical decision support tools that inform risk reduction interventions.

External Validation of an Algorithm to Identify Patients with High Data-Completeness in Electronic Health Records for Comparative Effectiveness Research.

OBJECTIVE: Electronic health records (EHR) data-discontinuity, i.e. receiving care outside of a particular EHR system, may cause misclassification of study variables. We aimed to validate an algorithm to identify patients with high EHR data-continuity to reduce such bias. MATERIALS AND METHODS: We analyzed data from two EHR systems linked with Medicare claims data from 2007 through 2014, one in Massachusetts (MA, n=80,588) and the other in North Carolina (NC, n=33,207). We quantified EHR data-continuity by Mean Proportion of Encounters Captured (MPEC) by the EHR system when compared to complete recording in claims data. The prediction model for MPEC was developed in MA and validated in NC. Stratified by predicted EHR data-continuity, we quantified misclassification of 40 key variables by Mean Standardized Differences (MSD) between the proportions of these variables based on EHR alone vs the linked claims-EHR data. RESULTS: The mean MPEC was 27% in the MA and 26% in the NC system. The predicted and observed EHR data-continuity was highly correlated (Spearman correlation=0.78 and 0.73, respectively). The misclassification (MSD) of 40 variables in patients of the predicted EHR data-continuity cohort was significantly smaller (44%, 95% CI: 40-48%) than that in the remaining population. DISCUSSION: The comorbidity profiles were similar in patients with high vs low EHR data-continuity. Therefore, restricting an analysis to patients with high EHR data-continuity may reduce information bias while preserving the representativeness of the study cohort. CONCLUSION: We have successfully validated an algorithm that can identify a high EHR data-continuity cohort representative of the source population.

Advancing the learning health system by incorporating social determinants.

The learning health system (LHS) has gained traction as a powerful framework for improving the cost and quality of healthcare. The goal of an LHS is to systematically integrate internal data and experience with external evidence so patients receive higher-quality, safer, and more efficient care. However, if the goal of an LHS is to improve health, as well as healthcare, it must account for and mitigate the negative impact of social and economic factors on health, known as the social determinants of health. In this paper, we discuss the critical role the LHS can play in addressing patients' social risk factors. We also discuss how integrating data on the social determinants and activities to reduce patients' social risk factors could advance the mission of the LHS to enhance patient engagement, improve the delivery of personalized care, and more accurately evaluate the effectiveness of care. Without the collection and integration of data on the social determinants of health, the LHS may fail to reach its full potential to improve health and healthcare.

Impact of structured interdisciplinary bedside rounding on patient outcomes at a large academic health centre.

BACKGROUND: Effective communication between healthcare providers and patients and their family members is an integral part of daily care and discharge planning for hospitalised patients. Several studies suggest that team-based care is associated with improved length of stay (LOS), but the data on readmissions are conflicting. Our study evaluated the impact of structured interdisciplinary bedside rounding (SIBR) on outcomes related to readmissions and LOS. METHODS: The SIBR team consisted of a physician and/or advanced practice provider, bedside nurse, pharmacist, social worker and bridge nurse navigator. Outcomes were compared in patients admitted to a hospital medicine unit using SIBR (n=1451) and a similar control unit (n=770) during the period of October 2016 to September 2017. Multivariable negative binomial regression analysis was used to compare LOS and logistic regression analysis was used to calculate 30-day and 7-day readmission in patients admitted to SIBR and control units, adjusting for covariates. RESULTS: Patients admitted to SIBR and control units were generally similar (p≥0.05) with respect to demographic and clinical characteristics. Unadjusted readmission rates in SIBR patients were lower than in control patients at both 30 days (16.6% vs 20.3%, p=0.03) and 7 days (6.3% vs 9.0%, p=0.02) after discharge, while LOS was similar. After adjusting for covariates, SIBR was not significantly related to the odds of 30-day readmission (OR 0.81, p=0.07) but was lower for 7-day readmission (OR 0.70, p=0.03); LOS was similar in both groups (p=0.58). CONCLUSION: SIBR did not reduce LOS and 30-day readmissions but had a significant impact on 7-day readmissions.

Food Insecurity Is Associated with an Increased Prevalence of Comorbid Medical Conditions in Obese Adults: NHANES 2007-2014.

BACKGROUND: Numerous studies have examined if food insecurity (FI) leads to increased weight gain, but little is known about how FI affects obese participants. OBJECTIVE: Our objective was to determine if obese, food-insecure adults are more likely to have medical comorbidities than obese, food-secure adults. DESIGN: We conducted a cross-sectional study using the 2007-2014 National Health and Nutrition Examination Survey (NHANES). PARTICIPANTS: All obese participants (≥ 20 years) in NHANES were eligible. Participants who were pregnant or missing FI data were excluded. MAIN MEASURES: The primary exposure was household FI, and the primary outcome was the total number of obesity-related comorbidities. Secondary outcomes evaluated the association between FI and individual comorbidities. Propensity score weighting was used to improve covariate balance. We used negative binomial regression to test the association between FI and the total number of comorbidities. We used logistic regression to test the association between FI and individual comorbidities. KEY RESULTS: Of the 9203 obese participants, 15.6% were food insecure. FI (ß = 0.09, 95% CI: 0.02, 0.15; p = 0.01) and very low food security (ß = 0.17, 95% CI: 0.07, 0.28; p = 0.003) were associated with an increased number of comorbidities. In secondary analyses, FI was associated with increased odds of coronary artery disease (OR: 1.5, 95% CI: 1.1, 2.0) and asthma (OR: 1.3, 95% CI: 1.1, 1.6). Very low food security was associated with increased odds of coronary artery disease, diabetes, and asthma. CONCLUSION: Obese adults living in food-insecure households were more likely to have an increased number of comorbid conditions than obese adults living in food-secure households. Clinicians should be aware of the association between FI and comorbid medical conditions when treating patients with obesity.

A federated EHR network data completeness tracking system.

OBJECTIVE: The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network. MATERIALS AND METHODS: The CTX applies a systems-based approach to design workflow and technology for assessing completeness across distributed electronic health record data repositories participating in a queryable, federated network. The CTX invokes 2 positive feedback loops that utilize open source tools (DQe-c and Vue) to integrate technology and human actors in a system geared for increasing capacity and taking action. A pilot implementation of the system involved 6 ARCH partner sites between January 2017 and May 2018. RESULTS: The ARCH CTX has enabled the network to monitor and, if needed, adjust its data management processes to maintain complete datasets for secondary use. The system allows the network and its partner sites to profile data completeness both at the network and partner site levels. Interactive visualizations presenting the current state of completeness in the context of the entire network as well as changes in completeness across time were valued among the CTX user base. DISCUSSION: Distributed clinical data networks are complex systems. Top-down approaches that solely rely on technology to report data completeness may be necessary but not sufficient for improving completeness (and quality) of data in large-scale clinical data networks. Improving and maintaining complete (high-quality) data in such complex environments entails sociotechnical systems that exploit technology and empower human actors to engage in the process of high-quality data curating. CONCLUSIONS: The CTX has increased the network's capacity to rapidly identify data completeness issues and empowered ARCH partner sites to get involved in improving the completeness of respective data in their repositories.

Empirical-Based Typology of Health Care Utilization by Medicare Eligible Veterans.

OBJECTIVE: Up to 70 percent of patients who receive care through Veterans Health Administration (VHA) facilities also receive care from non-VA providers. Using applied classification techniques, this study sought to improve understanding of how elderly VA patients use VA services and complementary use of non-VA care. METHODS: The study included 1,721,900 veterans age 65 and older who were enrolled in VA and Medicare during 2013 with at least one VA encounter during 2013. Outpatient and inpatient encounters and medications received in VA were classified, and mutually exclusive patient subsets distinguished by patterns of VA service use were derived empirically using latent class analysis (LCA). Patient characteristics and complementary use of non-VA care were compared by patient subset. RESULTS: Five patterns of VA service use were identified that were distinguished by quantity of VA medical and specialty services, medication complexity, and mental health services. Low VA Medical users tend to be healthier and rely on non-VA services, while High VA users have multiple high cost illnesses and concentrate their care in the VA. CONCLUSIONS: VA patients distinguished by patterns of VA service use differ in illness burden and the use of non-VA services. This information may be useful for framing efforts to optimize access to care and care coordination for elderly VA patients.

Perspectives on Electronic Informed Consent From Patients Underrepresented in Research in the United States: A Focus Group Study.

Digital informed consent may better inform individuals about health research and increase participation. In the United States and elsewhere, minorities and rural populations are underrepresented in health research and may benefit from well-designed electronic informed consent (eIC). Seven focus groups were conducted with 50 Caucasian, African American, and rural patients in the United States. Participants were asked their preferences for a paper versus electronic informed consent document. Participants found the e-version easier to use, more interesting, and better for understanding. Minority participants emphasized limited access, computer literacy, and trust barriers to eIC. Rural participants were concerned about accessibility, connectivity, privacy, and confidentiality. People see value in electronic consenting. Researchers should consider barriers to eIC among underrepresented populations before recruitment.

Sex and cardiovascular disease status differences in attitudes and willingness to participate in clinical research studies/clinical trials.

BACKGROUND: While women are under-represented in research on cardiovascular disease (CVD), little is known about the attitudes of men and women with CVD regarding participation in clinical research studies/clinical trials. METHODS: Patients with CVD (and/or risk factors) and patients with other chronic conditions from Iowa were recruited from a commercial panel. An on-line survey assessed willingness to participate (WTP) and other attitudes towards aspects of clinical research studies. RESULTS: Based on 504 respondents, there were no differences in WTP in patients with CVD compared to patients with other chronic diseases. Across all respondents, men had 14% lower WTP (relative risk (RR) for men, 0.86, 95% CI, 0.72-1.02). Among patients with CVD, there was no significant difference in WTP between women (RR for women = 1) and men (RR for men, 0.96, 95% CI, 0.82-1.14). There were no significant differences based on sex or CVD status for attitudes on randomization, blinding, side effects, conflict of interest, experimental treatments or willingness to talk to one's physician. Women had more favorable attitudes about participants being treated like "guinea pigs" (RR for men, 0.84, 95% CI, 0.73-0.98) and clinical trials being associated with terminally ill patients (RR for men, 0.93, 95% CI, 0.86-1.00). CONCLUSIONS: The findings reported here suggest that the observed lower levels of participation by women are due to factors other than a lower WTP or to women having more negative attitudes towards aspects of study participation. Patients with CVD have similar attitudes and WTP as patients with other chronic conditions.

Pragmatic clinical trials embedded in healthcare systems: generalizable lessons from the NIH Collaboratory.

BACKGROUND: The clinical research enterprise is not producing the evidence decision makers arguably need in a timely and cost effective manner; research currently involves the use of labor-intensive parallel systems that are separate from clinical care. The emergence of pragmatic clinical trials (PCTs) poses a possible solution: these large-scale trials are embedded within routine clinical care and often involve cluster randomization of hospitals, clinics, primary care providers, etc. Interventions can be implemented by health system personnel through usual communication channels and quality improvement infrastructure, and data collected as part of routine clinical care. However, experience with these trials is nascent and best practices regarding design operational, analytic, and reporting methodologies are undeveloped. METHODS: To strengthen the national capacity to implement cost-effective, large-scale PCTs, the Common Fund of the National Institutes of Health created the Health Care Systems Research Collaboratory (Collaboratory) to support the design, execution, and dissemination of a series of demonstration projects using a pragmatic research design. RESULTS: In this article, we will describe the Collaboratory, highlight some of the challenges encountered and solutions developed thus far, and discuss remaining barriers and opportunities for large-scale evidence generation using PCTs. CONCLUSION: A planning phase is critical, and even with careful planning, new challenges arise during execution; comparisons between arms can be complicated by unanticipated changes. Early and ongoing engagement with both health care system leaders and front-line clinicians is critical for success. There is also marked uncertainty when applying existing ethical and regulatory frameworks to PCTS, and using existing electronic health records for data capture adds complexity.

Gatekeepers as Care Providers: The Care Work of Patient-centered Medical Home Clerical Staff.

International implementation of the patient-centered medical home (PCMH) model for delivering primary care has dramatically increased in the last decade. A majority of research on PCMH's impact has emphasized the care provided by clinically trained staff. In this article, we report our ethnographic analysis of data collected from Department of Veterans Affairs staff implementing PACT, the VA version of PCMH. Teams were trained to use within-team delegation, largely accomplished through attention to clinical licensure, to differentiate staff in providing efficient, patient-centered care. In doing so, PACT may reinforce a clinically defined culture of care that countermands PCMH ideals. Such competing rubrics for care are brought into relief through a focus on the care work performed by clerks. Ethnographic analysis identifies clerks' care as a kind of emotional dirty work, signaling important areas for future anthropological study of the relationships among patient-centered care, stigma, and clinical authority.

The Critical Role of Clerks in the Patient-Centered Medical Home.

Research evaluating the effectiveness, function, and implementation of patient-centered medical homes (PCMHs) has found major socioprofessional transformations and contributions of primary care physicians and, to a lesser degree, nurses. Our longitudinal ethnographic research with teams implementing PCMH in Veterans Health Administration (VHA) primary care identifies the important but largely underutilized contributions of clerks to PCMH outcomes. Although the relationship of high-performing clerical staff to patient satisfaction is widely acknowledged, PCMH can be further enhanced by enabling clerks to use administrative tasks as conduits for investing in long-term personalized relationships with patients that foster trust in the PCMH and the broader health care organization. Such relationships are engendered through the care-coordination activities clerks perform, which may be bolstered by organizational investment in clerks as skilled health care team members.