奥密克戎变异株流行期间上海方舱医院军队医务人员心理健康状况及其影响因素分析

目的 调查严重急性呼吸综合征冠状病毒2(SARS-CoV-2)奥密克戎变异株流行期间上海方舱医院军队医务人员的心理健康状况及其影响因素。方法 采用9项患者健康问卷抑郁自评量表（PHQ-9）、7项广泛性焦虑障碍（GAD-7）量表、阿森斯失眠量表（AIS）对上海方舱医院的540名军队医务人员进行问卷调查,了解军队医务人员的心理健康状况,并采用logistic回归分析探讨其影响因素。结果 共回收有效问卷536份,回收有效率为99.3%(536/540)。上海方舱医院军队医务人员的抑郁、焦虑及失眠症状发生率分别为45.5%(244/536)、26.1%(140/536)和59.5%(319/536)。logistic回归分析结果显示,是否常驻上海、每日浏览信息中负面信息占比、进入方舱后饮食状况是抑郁、焦虑、失眠的影响因素（P均<0.05）,年龄、对未来上海的信心是抑郁和失眠的影响因素（P均<0.05）,每日花费在疫情相关信息上的时间是失眠的影响因素（P=0.021）。结论 SARS-CoV-2奥密克戎变异株流行期间上海方舱医院军队医务人员的抑郁、焦虑及失眠发生率较高,应定期持续监测...     

A decade post-HITECH: Critical access hospitals have electronic health records but struggle to keep up with other advanced functions

ObjectiveDespite broad electronic health record (EHR) adoption in U.S. hospitals, there is concern that an “advanced use” digital divide exists between critical access hospitals (CAHs) and non-CAHs. We measured EHR adoption and advanced use over time to analyzed changes in the divide.Materials and MethodsWe used 2008 to 2018 American Hospital Association Information Technology survey data to update national EHR adoption statistics. We stratified EHR adoption by CAH status and measured advanced use for both patient engagement (PE) and clinical data analytics (CDA) domains. We used a linear probability regression for each domain with year-CAH interactions to measure temporal changes in the relationship between CAH status and advanced use.ResultsIn 2018, 98.3% of hospitals had adopted EHRs; there were no differences by CAH status. A total of 58.7% and 55.6% of hospitals adopted advanced PE and CDA functions, respectively. In both domains, CAHs were less likely to be advanced users: 46.6% demonstrated advanced use for PE and 32.0% for CDA. Since 2015, the advanced use divide has persisted for PE and widened for CDA.DiscussionEHR adoption among hospitals is essentially ubiquitous; however, CAHs still lag behind in advanced use functions critical to improving care quality. This may be rooted in different advanced use needs among CAH patients and lack of access to technical expertise.ConclusionsThe advanced use divide prevents CAH patients from benefitting from a fully digitized healthcare system. To close the widening gap in CDA, policymakers should consider partnering with vendors to develop implementation guides and standards for functions like dashboards and high-risk patient identification algorithms to better support CAH adoption.     

dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling

ObjectiveTo develop a lossless distributed algorithm for generalized linear mixed model (GLMM) with application to privacy-preserving hospital profiling.Materials and MethodsThe GLMM is often fitted to implement hospital profiling, using clinical or administrative claims data. Due to individual patient data (IPD) privacy regulations and the computational complexity of GLMM, a distributed algorithm for hospital profiling is needed. We develop a novel distributed penalized quasi-likelihood (dPQL) algorithm to fit GLMM when only aggregated data, rather than IPD, can be shared across hospitals. We also show that the standardized mortality rates, which are often reported as the results of hospital profiling, can also be calculated distributively without sharing IPD. We demonstrate the applicability of the proposed dPQL algorithm by ranking 929 hospitals for coronavirus disease 2019 (COVID-19) mortality or referral to hospice that have been previously studied.ResultsThe proposed dPQL algorithm is mathematically proven to be lossless, that is, it obtains identical results as if IPD were pooled from all hospitals. In the example of hospital profiling regarding COVID-19 mortality, the dPQL algorithm reached convergence with only 5 iterations, and the estimation of fixed effects, random effects, and mortality rates were identical to that of the PQL from pooled data.ConclusionThe dPQL algorithm is lossless, privacy-preserving and fast-converging for fitting GLMM. It provides an extremely suitable and convenient distributed approach for hospital profiling.     

Predicting pressure injury using nursing assessment phenotypes and machine learning methods

ObjectivePressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure injury risk can significantly facilitate early prevention and treatment and avoid adverse outcomes. While many pressure injury risk assessment tools exist, most were developed before there was access to large clinical datasets and advanced statistical methods, limiting their accuracy. In this paper, we describe the development of machine learning-based predictive models, using phenotypes derived from nurse-entered direct patient assessment data.MethodsWe utilized rich electronic health record data, including full assessment records entered by nurses, from 5 different hospitals affiliated with a large integrated healthcare organization to develop machine learning-based prediction models for pressure injury. Five-fold cross-validation was conducted to evaluate model performance.ResultsTwo pressure injury phenotypes were defined for model development: nonhospital acquired pressure injury (N = 4398) and hospital acquired pressure injury (N = 1767), representing 2 distinct clinical scenarios. A total of 28 clinical features were extracted and multiple machine learning predictive models were developed for both pressure injury phenotypes. The random forest model performed best and achieved an AUC of 0.92 and 0.94 in 2 test sets, respectively. The Glasgow coma scale, a nurse-entered level of consciousness measurement, was the most important feature for both groups.ConclusionsThis model accurately predicts pressure injury development and, if validated externally, may be helpful in widespread pressure injury prevention.     

Health-seeking behaviour of the elderly living alone in an urbanised low-income community in Singapore

INTRODUCTION:Elderly persons who live alone are more likely to be socially isolated and at increased risk of adverse health outcomes, unnecessary hospital re-admissions and premature mortality. We aimed to understand the health-seeking behaviour of elderly persons living alone in public rental housing in Singapore.METHODS:In-depth interviews were conducted using a semi-structured question guide. Participants were selected using a purposive sampling approach. Interviews were conducted until theme saturation was reached. Qualitative data collected was analysed using manual thematic coding methods.RESULTS:Data analysis revealed five major themes: accessibility of healthcare services and financial assistance schemes; perceived high cost of care; self-management; self-reliance; and mismatch between perceived needs and services.CONCLUSION:Elderly persons living in one-room rental flats are a resilient and resourceful group that values self-reliance and independence. Most of the elderly who live alone develop self-coping mechanisms to meet their healthcare needs rather than seek formal medical consultation. The insightful findings from this study should be taken into consideration when models of healthcare delivery are being reviewed and designed so as to support the disadvantaged elderly living alone.     

城市公立医院综合改革背景下公立医院发展战略研究——基于SWOT分析方法

目的 明确在改革背景下城市公立医院的发展战略。方法 利用SWOT分析城市公立医院在城市公立医院综合改革背景下的发展优势、劣势、机遇和威胁。结果 城市公立医院的优势体现在人才、技术上,劣势是管理体制和运行机制不完善,面临的发展机遇是国家大力推动公立医院改革以及城镇化、疾病谱的改变,威胁是分级诊疗和社会办医的影响。结论 城市公立医院应推动管理体制和运行机制改革,并继续发挥在人才、技术上的优势,将服务定位于危急重症和疑难杂症等高层次医疗服务需求上。     

Can we understand population healthcare needs using electronic medical records?

INTRODUCTIONThe identification of population-level healthcare needs using hospital electronic medical records (EMRs) is a promising approach for the evaluation and development of tailored healthcare services. Population segmentation based on healthcare needs may be possible using information on health and social service needs from EMRs. However, it is currently unknown if EMRs from restructured hospitals in Singapore provide information of sufficient quality for this purpose. We compared the inter-rater reliability between a population segment that was assigned prospectively and one that was assigned retrospectively based on EMR review.METHODS200 non-critical patients aged ≥ 55 years were prospectively evaluated by clinicians for their healthcare needs in the emergency department at Singapore General Hospital, Singapore. Trained clinician raters with no prior knowledge of these patients subsequently accessed the EMR up to the prospective rating date. A similar healthcare needs evaluation was conducted using the EMR. The inter-rater reliability between the two rating sets was evaluated using Cohen’s Kappa and the incidence of missing information was tabulated.RESULTSThe inter-rater reliability for the medical ‘global impression’ rating was 0.37 for doctors and 0.35 for nurses. The inter-rater reliability for the same variable, retrospectively rated by two doctors, was 0.75. Variables with a higher incidence of missing EMR information such as ‘social support in case of need’ and ‘patient activation’ had poorer inter-rater reliability.CONCLUSIONPre-existing EMR systems may not capture sufficient information for reliable determination of healthcare needs. Thus, we should consider integrating policy-relevant healthcare need variables into EMRs.     

How Do Patients and Providers React to Different Incentives in the Chinese Multiple Health Security Systems?

Background:

China has achieved universal health insurance coverage. This study examined how patients and hospitals react to the different designs of the plans and to monitoring of patients by the local authority in the Chinese multiple health security schemes.

Methods:

The sample for analysis consisted of 1006 orthopedic inpatients who were admitted between January and December 2011 at a tertiary teaching hospital located in Beijing. We conducted general linear regression analyses to investigate whether medical expenditure and length of stay differed according to the different incentives.

Results:

Patients under plans with lower copayment rates consumed significantly more medication compared with those under plans with higher copayment rates. Under plans with an annual ceiling for insurance coverage, patients spent significantly more in the second half of the year than in the first half of the year. The length of stay was shorter among patients when there were government monitoring and a penalty to the hospital service provider.

Conclusions:

Our results indicate that the different designs and monitoring of the health security systems in China cause opportunistic behavior by patients and providers. Reformation is necessary to reduce those incentives, and improve equity and efficiency in healthcare use.     

Clinical and epidemiological characteristics of COVID-19 mortality in Saudi Arabia

Objectives:To analyze the clinical and epidemiological characteristics for 224 of in-hospital coronavirus disease 2019 (COVID-19) mortality cases. This study’s clinical implications provide insight into the significant death indicators among COVID-19 patients and the outbreak burden on the healthcare system in the Kingdom of Saudi Arabia (KSA).Methods:A multi-center retrospective cross-sectional study conducted among all COVID-19 mortality cases admitted to 15 Armed Forces hospitals across KSA, from March to July 2020. Demographic data, clinical presentations, laboratory investigations, and complications of COVID-19 mortality cases were collected and analyzed.Results:The mean age was 69.66±14.68 years, and 142 (63.4%) of the cases were male. Overall, 30% of the COVID-19 mortalities occurred in the first 24 hours of hospital admission, while 50% occurred on day 10. The most prevalent comorbidities were diabetes mellitus (DM, 73.7%), followed by hypertension (HTN, 69.6%). Logistic regression for risk factors in all mortality cases revealed that direct mortality cases from COVID-19 were more likely to develop acute respiratory distress syndrome (odds ratio [OR]: 1.75, confidence intervel [CI: 0.89-3.43]; p=0.102) and acute kidney injury (OR: 1.01, CI: [0.54-1.90]; p=0.960).Conclusion:Aging, male gender and the high prevalence of the underlying diseases such as, DM and HTN were a significant death indicators among COVID-19 mortality cases in KSA. Increases in serum ferritin, procalcitonin, C-reactive protein (CRP), and D-dimer levels can be used as indicators of disease progression.     

Application of Bayesian networks to generate synthetic health data

ObjectiveThis study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data.Materials and MethodsWe employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning tasks, preservation of rare events, disclosure risk, and the ability of a machine learning classifier to discriminate between the real and synthetic data.ResultsOur Bayesian network model outperformed or equaled medBGAN in all key metrics. Notable improvement was achieved in capturing rare variables and preserving association rules.DiscussionBayesian networks generated data sufficiently similar to the original data with minimal risk of disclosure, while offering additional transparency, computational efficiency, and capacity to handle more data types in comparison to existing methods. We hope this method will allow healthcare organizations to efficiently disseminate synthetic health data to researchers, enabling them to generate hypotheses and develop analytical tools. ConclusionWe conclude the application of Bayesian networks is a promising option for generating realistic synthetic health data that preserves the features of the original data without compromising data privacy.     

Use of a Regional Health Information Exchange to Detect Crossover of Patients with MRSA between Urban Hospitals

Background

A significant portion of patients already known to be colonized or infected with Methicillin-Resistant Staphylococcus aureus (MRSA) may not be identified at admission by neighboring hospitals.

Methods

We utilized data from a Regional Health Information Exchange to assess the frequency that patients known to have MRSA at one healthcare system are admitted to a neighboring healthcare system unaware of their MRSA status. We conducted a retrospective, registry trial from January 1999 through January 2006 involving three healthcare systems in central Indianapolis, representing six hospitals.

Results

Over one year, 286 unique patients generated 587 admissions accounting for 4,335 inpatient days where the receiving hospital was not aware of the prior history of MRSA. The patients accounted for an additional 10% of MRSA admissions received by study hospitals over one year and over 3,600 inpatient days without contact isolation.

Conclusions

Information exchange could improve timely identification of known MRSA patients within an urban setting.     

Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods

ObjectivesThe coronavirus disease 2019 (COVID-19) is a resource-intensive global pandemic. It is important for healthcare systems to identify high-risk COVID-19-positive patients who need timely health care. This study was conducted to predict the hospitalization of older adults who have tested positive for COVID-19.MethodsWe screened all patients with COVID test records from 11 Mass General Brigham hospitals to identify the study population. A total of 1495 patients with age 65 and above from the outpatient setting were included in the final cohort, among which 459 patients were hospitalized. We conducted a clinician-guided, 3-stage feature selection, and phenotyping process using iterative combinations of literature review, clinician expert opinion, and electronic healthcare record data exploration. A list of 44 features, including temporal features, was generated from this process and used for model training. Four machine learning prediction models were developed, including regularized logistic regression, support vector machine, random forest, and neural network.ResultsAll 4 models achieved area under the receiver operating characteristic curve (AUC) greater than 0.80. Random forest achieved the best predictive performance (AUC = 0.83). Albumin, an index for nutritional status, was found to have the strongest association with hospitalization among COVID positive older adults.ConclusionsIn this study, we developed 4 machine learning models for predicting general hospitalization among COVID positive older adults. We identified important clinical factors associated with hospitalization and observed temporal patterns in our study cohort. Our modeling pipeline and algorithm could potentially be used to facilitate more accurate and efficient decision support for triaging COVID positive patients.     

Assessing hospital electronic health record vendor performance across publicly reported quality measures

ObjectiveLittle is known regarding variation among electronic health record (EHR) vendors in quality performance. This issue is compounded by selection effects in which high-quality hospitals coalesce to a subset of market leading vendors. We measured hospital performance, stratified by EHR vendor, across 4 quality metrics.Materials and MethodsWe used data on 1272 hospitals in 2018 across 4 quality measures: Leapfrog Computerized Provider Order Entry/EHR Evaluation, Centers for Medicare and Medicaid Services Hospital Compare Star Ratings, Hospital-Acquired Condition (HAC) score, and Hospital Readmission Reduction Program (HRRP) ratio. We examined score distributions and used multivariable regression to evaluate the association between vendor and score, recovering partial R² to assess the proportion of quality variation explained by vendor.ResultsWe found significant variation across and within EHR vendors. The largest vendor, vendor A, had the highest mean score on the Leapfrog Computerized Provider Order Entry/EHR Evaluation and HRRP ratio, vendor G had the highest Hospital Compare score, and vendor F had the highest HAC score. In adjusted models, no vendor was significantly associated with higher performance on more than 2 measures. EHR vendor explained between 1.2% (HAC) and 7.6 (HRRP) of the variation in quality performance.DiscussionNo EHR vendor was associated with higher quality across all measures, and the 2 largest vendors were not associated with the highest scores. Only a small fraction of quality variation was explained by EHR vendor choice.ConclusionsTop performance on quality measures can be achieved with any EHR vendor; much of quality performance is driven by the hospital and how it uses the EHR.     

A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions

Background

Data-driven risk stratification models built using data from a single hospital often have a paucity of training data. However, leveraging data from other hospitals can be challenging owing to institutional differences with patients and with data coding and capture.

Objective

To investigate three approaches to learning hospital-specific predictions about the risk of hospital-associated infection with Clostridium difficile, and perform a comparative analysis of the value of different ways of using external data to enhance hospital-specific predictions.

Materials and methods

We evaluated each approach on 132 853 admissions from three hospitals, varying in size and location. The first approach was a single-task approach, in which only training data from the target hospital (ie, the hospital for which the model was intended) were used. The second used only data from the other two hospitals. The third approach jointly incorporated data from all hospitals while seeking a solution in the target space.

Results

The relative performance of the three different approaches was found to be sensitive to the hospital selected as the target. However, incorporating data from all hospitals consistently had the highest performance.

Discussion

The results characterize the challenges and opportunities that come with (1) using data or models from collections of hospitals without adapting them to the site at which the model will be used, and (2) using only local data to build models for small institutions or rare events.

Conclusions

We show how external data from other hospitals can be successfully and efficiently incorporated into hospital-specific models.     

Automated Surveillance for Adverse Drug Events at a Community Hospital and an Academic Medical Center

ObjectivesTo compare the rates and nature of ADEs at an academic medical center and a community hospital using a single computerized ADE surveillance system.DesignProspective cohort study of patients admitted to two tertiary care hospitals.Outcome MeasureAdverse drug events identified by automated surveillance and voluntary reporting.MethodsWe implemented an automated surveillance system across an academic medical center and a community hospital. Potential events identified by the computer were reviewed in detail by medication safety pharmacists and scored for causality and severity. Findings were compared between the two hospitals, and with voluntary reports from nurses and pharmacists.ResultsOver the 8 month study period, 25,177 patients were admitted to the university hospital and 8,029 to the community hospital. There were 1,116 ADEs in 900 patients at the university hospital for an overall rate of 4.4 ADEs per 100 admissions. At the community hospital, 399 patients experienced 501 ADEs for a rate of 6.2 events per 100 admissions. Rates of antibiotic-associated colitis, drug-induced hypoglycemia, and anticoagulation-related ADEs were significantly higher at the community hospital compared with the university hospital. Computerized surveillance detected ADEs at a rate 3.6 times that of voluntary reporting at the university hospital and 12.3 times that at the community hospital.ConclusionsOperation of a common automated ADE surveillance system across hospitals permits meaningful comparison of ADE rates in different inpatient settings. Automated surveillance detects ADEs at rates far higher than voluntary reporting, and the difference may be greater in the community hospital setting. Community hospitals may experience higher rates of certain types of ADEs compared with academic medical centers.     

Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals

ObjectiveFederated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. “Personalized” FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described Coronavirus Disease 19 (COVID-19) diagnostic model. Additionally, to investigate the effect of system heterogeneity, we evaluate the performance of 4 FL variations.Materials and methodsWe leverage a FL healthcare collaborative including data from 5 international healthcare systems (US and Europe) encompassing 42 hospitals. We implemented a COVID-19 computer vision diagnosis system using the Federated Averaging (FedAvg) algorithm implemented on Clara Train SDK 4.0. To study the effect of data heterogeneity, training data was pooled from 3 systems locally and federation was simulated. We compared a centralized/pooled model, versus FedAvg, and 3 personalized FL variations (FedProx, FedBN, and FedAMP).ResultsWe observed comparable model performance with respect to internal validation (local model: AUROC 0.94 vs FedAvg: 0.95, P = .5) and improved model generalizability with the FedAvg model (P < .05). When investigating the effects of model heterogeneity, we observed poor performance with FedAvg on internal validation as compared to personalized FL algorithms. FedAvg did have improved generalizability compared to personalized FL algorithms. On average, FedBN had the best rank performance on internal and external validation.ConclusionFedAvg can significantly improve the generalization of the model compared to other personalization FL algorithms; however, at the cost of poor internal validity. Personalized FL may offer an opportunity to develop both internal and externally validated algorithms.     

Different roles with different goals: Designing to support shared situational awareness between patients and clinicians in the hospital

ObjectiveTeam situational awareness helps to ensure high-quality care and prevent errors in the complex hospital environment. Although extensive work has examined factors that contribute to breakdowns in situational awareness among clinicians, patients’ and caregivers’ roles have been neglected. To address this gap, we studied team-based situational awareness from the perspective of patients and their caregivers.Materials and MethodsWe utilized a mixed-methods approach, including card sorting and semi-structured interviews with hospitalized patients and their caregivers at a pediatric hospital and an adult hospital. We analyzed the results utilizing the situational awareness (SA) theoretical framework, which identifies 3 distinct stages: (1) perception of a signal, (2) comprehension of what the signal means, and (3) projection of what will happen as a result of the signal.ResultsA total of 28 patients and 19 caregivers across the 2 sites participated in the study. Our analysis uncovered how team SA helps patients and caregivers ensure that their values are heard, their autonomy is supported, and their clinical outcomes are the best possible. In addition, our participants described both barriers—such as challenges with communication—and enablers to facilitating shared SA in the hospital.DiscussionPatients and caregivers possess critical knowledge, expertise, and values required to ensure successful and accurate team SA. Therefore, hospitals need to incorporate tools that facilitate patients and caregivers as key team members for effective SA.ConclusionsElevating patients and caregivers from passive recipients to equal contributors and members of the healthcare team will improve SA and ensure the best possible outcomes.     

合肥市某口腔医院学龄前儿童龋齿发病率和致病因素调查分析

目的 分析某口腔医院学龄前儿童的龋齿患病率和影响因素,为学龄前儿童龋病的防治提供依据。方法 回顾性分析2020年1月至2023年7月来合肥市口腔医院问诊的154名学龄前儿童龋病相关资料,根据口腔健康状况分为有龋组(n=124名)和无龋组(n=30名),采用单因素和多因素logistic回归分析学龄前儿童龋病的影响因素。结果 最终入组154名儿童,龋病率为80.52%。有龋组年龄和女性、喜食甜食、公立医院就诊占比均高于无龋组,父母从事医疗工作、具备口腔知识以及定期口腔检查占比低于无龋组,差异均有统计学意义(P<0.05)。logistic回归结果显示,年龄增加(OR=1.646,95%CI:1.158~2.341)、喜食甜食(OR=0.226,95%CI:0.066~0.775)和在公立医院就诊(OR=3.377,95%CI:1.037~1.173)是学龄前儿童龋病发生的危险因素;父母具备口腔知识(OR=3.268,95%CI:1.072~9.965)是其保护因素。结论 学龄前儿童龋病发病率较高,与年龄呈正相关且好发于女性,可从加强儿童饮食控制、提高父母口腔知识水平、提升非公立医院诊疗能力等方面对龋病进行预防控制。     

A systematic literature review of Native American and Pacific Islanders’ perspectives on health data privacy in the United States

BackgroundPrivacy-related concerns can prevent equitable participation in health research by US Indigenous communities. However, studies focused on these communities'' views regarding health data privacy, including systematic reviews, are lacking.MethodsWe conducted a systematic literature review analyzing empirical, US-based studies involving American Indian/Alaska Native (AI/AN) and Native Hawaiian or other Pacific Islander (NHPI) perspectives on health data privacy, which we define as the practice of maintaining the security and confidentiality of an individual’s personal health records and/or biological samples (including data derived from biological specimens, such as personal genetic information), as well as the secure and approved use of those data.ResultsTwenty-one studies involving 3234 AI/AN and NHPI participants were eligible for review. The results of this review suggest that concerns about the privacy of health data are both prevalent and complex in AI/AN and NHPI communities. Many respondents raised concerns about the potential for misuse of their health data, including discrimination or stigma, confidentiality breaches, and undesirable or unknown uses of biological specimens.ConclusionsParticipants cited a variety of individual and community-level concerns about the privacy of their health data, and indicated that these deter their willingness to participate in health research. Future investigations should explore in more depth which health data privacy concerns are most salient to specific AI/AN and NHPI communities, and identify the practices that will make the collection and use of health data more trustworthy and transparent for participants.     

Doctors documenting: an ethnographic and informatics approach to understanding attending physician documentation in the pediatric emergency department

ObjectiveThe pediatric emergency department is a highly complex and evolving environment. Despite the fact that physicians spend a majority of their time on documentation, little research has examined the role of documentation in provider workflow. The aim of this study is to examine the task of attending physician documentation workflow using a mixed-methods approach including focused ethnography, informatics, and the Systems Engineering Initiative for Patient Safety (SEIPS) model as a theoretical framework.Materials and MethodsIn a 2-part study, we conducted a hierarchical task analysis of patient flow, followed by a survey of documenting ED providers. The second phase of the study included focused ethnographic observations of ED attendings which included measuring interruptions, time and motion, documentation locations, and qualitative field notes. This was followed by analysis of documentation data from the electronic medical record system.ResultsOverall attending physicians reported low ratings of documentation satisfaction; satisfaction after each shift was associated with busyness and resident completion. Documentation occurred primarily in the provider workrooms, however strategies such as bedside documentation, dictation, and multitasking with residents were observed. Residents interrupted attendings more often but also completed more documentation actions in the electronic medical record.DiscussionOur findings demonstrate that complex work processes such as documentation, cannot be measured with 1 single data point or statistical analysis but rather a combination of data gathered from observations, surveys, comments, and thematic analyses.ConclusionUtilizing a sociotechnical systems framework and a mixed-methods approach, this study provides a holistic picture of documentation workflow. This approach provides a valuable foundation not only for researchers approaching complex healthcare systems but also for hospitals who are considering implementing large health information technology projects.