How much can we trust electronic health record data?

Health Care Delivery Site- and Patient-Level Factors Associated With COVID-19 Primary Vaccine Series Completion in a National Network of Community Health Centers

Objectives. To assess multilevel factors associated with variation in COVID-19 vaccination rates in a US network of community health centers. Methods. Using multilevel logistic regression with electronic health record data from ADVANCE (Accelerating Data Value Across a National Community Health Center Network; January 1, 2022-December 31, 2022), we assessed associations between health care delivery site-level (n = 1219) and patient-level (n = 1 864 007) characteristics and COVID-19 primary vaccine series uptake. Results. A total of 1 337 440 patients completed the COVID-19 primary vaccine series. Health care delivery site characteristics were significantly associated with lower series completion rates, including being located in non-Medicaid expansion states and isolated or rural communities and serving fewer patients. Patient characteristics associated with significantly lower likelihood of completing the vaccine series included being Black/African American or American Indian/Alaska Native (vs White), younger age, lower income, being uninsured or publicly insured (vs using private insurance), and having fewer visits. Conclusions. Both health care delivery site- and patient-level factors were significantly associated with lower COVID-19 vaccine uptake. Community health centers have been a critical resource for vaccination during the pandemic. (Am J Public Health. 2024;114(11):1242-1251. https://doi.org/10.2105/AJPH.2024.307773).

Insomnia and unhealthy alcohol use in a National Sample of Women Veterans 50 years and older enrolled in the Veterans Health Administration

In this study, we examined rates of insomnia and co-occurring unhealthy alcohol use in a national sample of women Veterans age 50 years and older. We further explored associations between sociodemographic measures, insomnia-related clinical characteristics, and unhealthy alcohol use, and analyzed whether women with insomnia were more likely to report unhealthy alcohol use. Study aims were evaluated using national Veterans Health Administration (VA) electronic health records data from VA's Corporate Data Warehouse. Data were extracted for women Veterans ≥50 years old with ≥1 VA primary care visit in each study year (2018: 3/11/18-3/10/19; 2020: 3/11/20-3/10/21; 2022: 3/11/22-3/10/23). Cases of insomnia were identified via diagnostic codes and prescription medications for insomnia. Unhealthy alcohol use was identified via Alcohol Use Disorders Identification Test-Consumption screening scores indicating unhealthy alcohol use. Annual sample sizes ranged from 240,420-302,047. Over the study timeframe, insomnia rates (diagnosis or medication) among women ≥50 years old ranged from 18.11-19.29%; co-occurring insomnia and unhealthy alcohol use rates ranged from 2.02-2.52%. Insomnia and unhealthy alcohol use rates were highest among women aged 50-59 years old. Depression and physical health comorbidities were consistently associated with insomnia; associations by race and ethnicity were less consistent. Compared to women without insomnia, women Veterans with either concurrent or unremitting insomnia were more likely to endorse unhealthy alcohol use. Findings signal a potential need for assessment and preventative efforts aimed at addressing insomnia and unhealthy alcohol use among women Veterans.

Exploring potential drivers of patient engagement with their health data through digital platforms: A scoping review

BACKGROUND

Patient engagement when providing patient access to health data results from an interaction between the available tools and individual capabilities. The recent digital advancements of the healthcare field have altered the manifestation and importance of patient engagement. However, a comprehensive assessment of what factors contribute to patient engagement remain absent. In this review article, we synthesised the most frequently discussed factors that can foster patient engagement with their health data.

METHODS

A scoping review was conducted in MEDLINE, Embase, and Google Scholar. Relevant data were synthesized within 7 layers using a thematic analysis: (1) social and demographic factors, (2) patient ability factors, (3) patient motivation factors, (4) factors related to healthcare professionals' attitudes and skills, (5) health system factors, (6) technological factors, and (7) policy factors.

RESULTS

We identified 5801 academic and 200 Gy literature records, and included 292 (4.83%) in this review. Overall, 44 factors that can affect patient engagement with their health data were extracted. We extracted 6 social and demographic factors, 6 patient ability factors, 12 patient motivation factors, 7 factors related to healthcare professionals' attitudes and skills, 4 health system factors, 6 technological factors, and 3 policy factors.

CONCLUSIONS

Improving patient engagement with their health data enables the development of patient-centered healthcare, though it can also exacerbate existing inequities. While expanding patient access to health data is an important step towards fostering shared decision-making in healthcare and subsequently empowering patients, it is important to ensure that these developments reach all sectors of the community.

Implementation of a structured oral hygiene program through nursing assistant education to address non-ventilator hospital-acquired pneumonia: A quasi-experimental study

INTRODUCTION

Non-ventilator hospital-acquired pneumonia (NV HAP) is a common complication for hospitalized patients. NV HAP develops when patients aspirate oral secretions containing pathogenic bacteria. Appropriate oral hygiene can help mitigate NV HAP development. Hospital staff, including nursing assistants, play an important role in ensuring that these cares are completed.

DESIGN

A quasi-experimental pre-post design was used to evaluate outcomes before and after implementation of a structured oral hygiene education program.

METHODS

A structured oral hygiene program was developed and implemented in a large quaternary hospital. Change in NA knowledge, attitudes, and behaviors before and after implementation of the oral hygiene program was evaluated. Retrospective patient outcomes before and after the intervention were analyzed to detect changes in NV HAP rates.

RESULTS

Following the education, nursing assistant knowledge of recommended frequency of oral care for patients who are NPO increased (67.2% vs. 82.1%, p = 0.003). NAs were more likely to report oral hygiene tools including oral suctioning (80.8% vs. 90.2%, p = 0.005) and toothbrushes (89.3% vs. 95.3%, p = 0.031). The unadjusted incidence of NV HAP was significantly lower in the post-intervention cohort (0.25%) compared to the pre-intervention cohort (0.74%), p < 0.001. In the adjusted model, non-invasive positive pressure ventilation increased the odds of NV HAP by nearly sevenfold (AOR = 6.88, 95% CI: 3.99, 11.39).

CONCLUSION

Focused education for NAs is an effective strategy to increase knowledge related to oral hygiene. Implementing a structured oral hygiene program for NAs appears to be a promising practice to decrease NV HAP.

Uncovering Harmonization Potential in Health Care Data Through Iterative Refinement of Fast Healthcare Interoperability Resources Profiles Based on Retrospective Discrepancy Analysis: Case Study

BACKGROUND

Cross-institutional interoperability between health care providers remains a recurring challenge worldwide. The German Medical Informatics Initiative, a collaboration of 37 university hospitals in Germany, aims to enable interoperability between partner sites by defining Fast Healthcare Interoperability Resources (FHIR) profiles for the cross-institutional exchange of health care data, the Core Data Set (CDS). The current CDS and its extension modules define elements representing patients' health care records. All university hospitals in Germany have made significant progress in providing routine data in a standardized format based on the CDS. In addition, the central research platform for health, the German Portal for Medical Research Data feasibility tool, allows medical researchers to query the available CDS data items across many participating hospitals.

OBJECTIVE

In this study, we aimed to evaluate a novel approach of combining the current top-down generated FHIR profiles with the bottom-up generated knowledge gained by the analysis of respective instance data. This allowed us to derive options for iteratively refining FHIR profiles using the information obtained from a discrepancy analysis.

METHODS

We developed an FHIR validation pipeline and opted to derive more restrictive profiles from the original CDS profiles. This decision was driven by the need to align more closely with the specific assumptions and requirements of the central feasibility platform's search ontology. While the original CDS profiles offer a generic framework adaptable for a broad spectrum of medical informatics use cases, they lack the specificity to model the nuanced criteria essential for medical researchers. A key example of this is the necessity to represent specific laboratory codings and values interdependencies accurately. The validation results allow us to identify discrepancies between the instance data at the clinical sites and the profiles specified by the feasibility platform and addressed in the future.

RESULTS

A total of 20 university hospitals participated in this study. Historical factors, lack of harmonization, a wide range of source systems, and case sensitivity of coding are some of the causes for the discrepancies identified. While in our case study, Conditions, Procedures, and Medications have a high degree of uniformity in the coding of instance data due to legislative requirements for billing in Germany, we found that laboratory values pose a significant data harmonization challenge due to their interdependency between coding and value.

CONCLUSIONS

While the CDS achieves interoperability, different challenges for federated data access arise, requiring more specificity in the profiles to make assumptions on the instance data. We further argue that further harmonization of the instance data can significantly lower required retrospective harmonization efforts. We recognize that discrepancies cannot be resolved solely at the clinical site; therefore, our findings have a wide range of implications and will require action on multiple levels and by various stakeholders.

Prior COVID-19 vaccination and reduced risk of cerebrovascular diseases among COVID-19 survivors

The effects of COVID-19 vaccination on short-term and long-term cerebrovascular risks among COVID-19 survivors remained unknown. We conducted a national multi-center retrospective cohort study with 151 597 vaccinated and 151 597 unvaccinated COVID-19 patients using the TriNetX database, from January 1, 2020 to December 31, 2023. Patients baseline characteristics were balanced with propensity score matching (PSM). The outcomes were incident cerebrovascular diseases occurred between 1st and 30th days (short-term) after COVID-19 diagnosis. Nine subgroup analyses were conducted to explore potential effect modifications. We performed six sensitivity analyses, including evaluation of outcomes between 1st to 180th days, accounting for competing risk, and incorporating different variant timeline to test the robustness of our results. Kaplan-Meier curves and Log-Rank tests were performed to evaluate survival difference. Cox proportional hazards regressions were adopted to estimate the PSM-adjusted hazard ratios (HR). The overall short-term cerebrovascular risks were lower in the vaccinated group compared to the unvaccinated group (HR: 0.66, 95% CI: 0.56-0.77), specifically cerebral infarction (HR: 0.62, 95% CI: 0.48-0.79), occlusion and stenosis of precerebral arteries (HR: 0.74, 95% CI: 0.53-0.98), other cerebrovascular diseases (HR: 0.57, 95% CI: 0.42-0.77), and sequelae of cerebrovascular disease (HR: 0.39, 95% CI:0.23-0.68). Similarly, the overall cerebrovascular risks were lower in those vaccinated among most subgroups. The long-term outcomes, though slightly attenuated, were consistent (HR: 0.80, 95% CI: 0.73-0.87). Full 2-dose vaccination was associated with a further reduced risk of cerebrovascular diseases (HR: 0.63, 95% CI: 0.50-0.80) compared to unvaccinated patients. Unvaccinated COVID-19 survivors have significantly higher cerebrovascular risks than their vaccinated counterparts. Thus, clinicians are recommended to monitor this population closely for stroke events during postinfection follow-up.

Performance Data Advocacy for Continuing Professional Development in Health Professions

ABSTRACT

Efforts to optimize continuing professional development (CPD) are ongoing and include advocacy for the use of clinician performance data. Several educational and quality-based frameworks support the use of performance data to achieve intended improvement outcomes. Although intuitively appealing, the role of performance data for CPD has been uncertain and its utility mainly assumed. In this Scholarly Perspective, the authors briefly review and trace arguments that have led to the conclusion that performance data are essential for CPD. In addition, they summarize and synthesize a recent and ongoing research program exploring the relationship physicians have with performance data. They draw on Collins, Onwuegbuzie, and Johnson's legitimacy model and Dixon-Woods' integrative approach to generate inferences and ways of moving forward. This interpretive approach encourages questioning or raising of assumptions about related concepts and draws on the perspectives (i.e., interpretive work) of the research team to identify the most salient points to guide future work. The authors identify 6 stimuli for future programs of research intended to support broader and better integration of performance data for CPD. Their aims are to contribute to the discourse on data advocacy for CPD by linking conceptual, methodologic, and analytic processes and to stimulate discussion on how to proceed on the issue of performance data for CPD purposes. They hope to move the field from a discussion on the utility of data for CPD to deeper integration of relevant conceptual frameworks.

Process analysis of the patient pathway for automated data collection: an exemplar using pituitary surgery

Introduction

Automation of routine clinical data shows promise in relieving health systems of the burden associated with manual data collection. Identifying consistent points of documentation in the electronic health record (EHR) provides salient targets to improve data entry quality. Using our pituitary surgery service as an exemplar, we aimed to demonstrate how process mapping can be used to identify reliable areas of documentation in the patient pathway to target structured data entry interventions.

Materials and methods

This mixed methods study was conducted in the largest pituitary centre in the UK. Purposive snowball sampling identified frontline stakeholders for process mapping to produce a patient pathway. The final patient pathway was subsequently validated against a real-world dataset of 50 patients who underwent surgery for pituitary adenoma. Events were categorized by frequency and mapped to the patient pathway to determine critical data points.

Results

Eighteen stakeholders encompassing all members of the multidisciplinary team (MDT) were consulted for process mapping. The commonest events recorded were neurosurgical ward round entries (N = 212, 14.7%), pituitary clinical nurse specialist (CNS) ward round entries (N = 88, 6.12%) and pituitary MDT treatment decisions (N = 88, 6.12%) representing critical data points. Operation notes and neurosurgical ward round entries were present for every patient. 43/44 (97.7%) had a pre-operative pituitary MDT entry, pre-operative clinic letter, a post-operative clinic letter, an admission clerking entry, a discharge summary, and a post-operative histopathology pituitary multidisciplinary (MDT) team entries.

Conclusion

This is the first study to produce a validated patient pathway of patients undergoing pituitary surgery, serving as a comparison to optimise this patient pathway. We have identified salient targets for structured data entry interventions, including mandatory datapoints seen in every admission and have also identified areas to improve documentation adherence, both of which support movement towards automation.

Training the next generation of delivery science researchers: 10-year experience of a post-doctoral research fellowship program within an integrated care system

Introduction

Learning health systems require a workforce of researchers trained in the methods of identifying and overcoming barriers to effective, evidence-based care. Most existing postdoctoral training programs, such as NIH-funded postdoctoral T32 awards, support basic and epidemiological science with very limited focus on rigorous delivery science methods for improving care. In this report, we present the 10-year experience of developing and implementing a Delivery Science postdoctoral fellowship embedded within an integrated health care delivery system.

Methods

In 2012, the Kaiser Permanente Northern California Division of Research designed and implemented a 2-year postdoctoral Delivery Science Fellowship research training program to foster research expertise in identifying and addressing barriers to evidence-based care within health care delivery systems.

Results

Since 2014, 20 fellows have completed the program. Ten fellows had PhD-level scientific training, and 10 fellows had clinical doctorates (eg, MD, RN/PhD, PharmD). Fellowship alumni have graduated to faculty research positions at academic institutions (9), and research or clinical organizations (4). Seven alumni now hold positions in Kaiser Permanente's clinical operations or medical group (7).

Conclusions

This delivery science fellowship program has succeeded in training graduates to address delivery science problems from both research and operational perspectives. In the next 10 years, additional goals of the program will be to expand its reach (eg, by developing joint research training models in collaboration with clinical fellowships) and strengthen mechanisms to support transition from fellowship to the workforce, especially for researchers from underrepresented groups.

Prospective acceptability of digital phenotyping among pregnant and parenting people with opioid use disorder: A multisite qualitative study

Background

While medications for opioid use disorder (MOUD) effectively treat OUD during pregnancy and the postpartum period, poor treatment retention is common. Digital phenotyping, or passive sensing data captured from personal mobile devices, namely smartphones, provides an opportunity to understand behaviors, psychological states, and social influences contributing to perinatal MOUD non-retention. Given this novel area of investigation, we conducted a qualitative study to determine the acceptability of digital phenotyping among pregnant and parenting people with opioid use disorder (PPP-OUD).

Methods

This study was guided by the Theoretical Framework of Acceptability (TFA). Within a clinical trial testing a behavioral health intervention for PPP-OUD, we used purposeful criterion sampling to recruit 11 participants who delivered a child in the past 12  months and received OUD treatment during pregnancy or the postpartum period. Data were collected through phone interviews using a structured interview guide based on four TFA constructs (affective attitude, burden, ethicality, self-efficacy). We used framework analysis to code, chart, and identify key patterns within the data.

Results

Participants generally expressed positive attitudes about digital phenotyping and high self-efficacy and low anticipated burden to participate in studies that collect smartphone-based passive sensing data. Nonetheless, concerns were noted related to data privacy/security and sharing location information. Differences in participant assessments of burden were related to length of time required and level of remuneration to participate in a study. Interviewees voiced broad support for participating in a digital phenotyping study with known/trusted individuals but expressed concerns about third-party data sharing and government monitoring.

Conclusion

Digital phenotyping methods were acceptable to PPP-OUD. Enhancements in acceptability include allowing participants to maintain control over which data are shared, limiting frequency of research contacts, aligning compensation with participant burden, and outlining data privacy/security protections on study materials.

Digital Health Data Quality Issues: Systematic Review

BACKGROUND

The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact.

OBJECTIVE

The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ?

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework.

RESULTS

The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes.

CONCLUSIONS

The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.

A Qualitative Study to Understand the Cultural Factors That Influence Clinical Data Use for Continuing Professional Development

INTRODUCTION

The use of data to inform lifelong learning has become increasingly important in continuing professional development (CPD) practice. Despite the potential benefits of data-driven learning, our understanding of how physicians engage in data-informed learning activities, particularly for CPD, remains unclear and warrants further study. The purpose of this study was to explore how physicians perceive cultural factors (individual, organizational, and systemic) that influence the use of clinical data to inform lifelong learning and self-initiated CPD activities.

METHODS

This qualitative study is part of an explanatory sequential mixed-methods study examining data-informed learning. Participants were psychiatrists and general surgeons from Canada and the United States. Recruitment occurred between April 2019 and November 2019, and the authors conducted semistructured telephone interviews between May 2019 and November 2019. The authors performed thematic analysis using an iterative, inductive method of constant comparative analysis.

RESULTS

The authors interviewed 28 physicians: 17 psychiatrists (61%) and 11 general surgeons (39%). Three major themes emerged from the continuous, iterative analysis of interview transcripts: (1) a strong relationship between data and trust, (2) a team-based approach to data-informed learning for practice improvement, and (3) a need for organizational support and advocacy to put data into practice.

CONCLUSION

Building trust, taking a team-based approach, and engaging multiple stakeholders, such as data specialists and organizational leadership, may significantly improve the use of data-informed learning. The results are situated in the existing literature, and opportunities for future research are summarized.

Future of Teleoncology: Trends and Disparities in Telehealth and Secure Message Utilization in the COVID-19 Era

PURPOSE

The COVID-19 pandemic created an imperative to re-examine the role of telehealth in oncology. We studied trends and disparities in utilization of telehealth (video and telephone visits) and secure messaging (SM; ie, e-mail via portal/app), before and during the pandemic.

METHODS

Retrospective cohort study of hematology/oncology patient visits (telephone/video/office) and SM between January 1, 2019, and September 30, 2020, at Kaiser Permanente Northern California.

RESULTS

Among 334,666 visits and 1,161,239 SM, monthly average office visits decreased from 10,562 prepandemic to 1,769 during pandemic, telephone visits increased from 5,114 to 8,663, and video visits increased from 40 to 4,666. Monthly average SM increased from 50,788 to 64,315 since the pandemic began. Video visits were a significantly higher fraction of all visits (P < .01) in (1) younger patients (Generation Z 48%, Millennials 46%; Generation X 40%; Baby Boomers 34.4%; Silent Generation 24.5%); (2) patients with commercial insurance (39%) compared with Medicaid (32.7%) or Medicare (28.1%); (3) English speakers (33.7%) compared with those requiring an interpreter (24.5%); (4) patients who are Asian (35%) and non-Hispanic White (33.7%) compared with Black (30.1%) and Hispanic White (27.5%); (5) married/domestic partner patients (35%) compared with single/divorced/widowed (29.9%); (6) Charlson comorbidity index ≤ 3 (36.2%) compared with > 3 (31.3%); and (7) males (34.6%) compared with females (32.3%). Similar statistically significant SM utilization patterns were also seen.

CONCLUSION

In the pandemic era, hematology/oncology telehealth and SM use rapidly increased in a manner that is feasible and sustained. Possible disparities existed in video visit and SM use by age, insurance plan, language, race, ethnicity, marital status, comorbidities, and sex.

Can decision support combat incompleteness and bias in routine primary care data?

OBJECTIVE

Routine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.

MATERIALS AND METHODS

We used the clinical documentation of 34 UK general practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician's final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding.

RESULTS

Supported documentation contained significantly more codes (incidence rate ratio [IRR] = 5.76 [4.31, 7.70] P < .001) and less free text (IRR = 0.32 [0.27, 0.40] P < .001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b = -0.08 [-0.11, -0.05] P < .001) in the supported consultations, and this was the case for both codes and free text.

CONCLUSIONS

We provide evidence that data entry in the EHR is incomplete and reflects physicians' cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.