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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
26 to 50 of 731 Research Studies DisplayedHuo T, Glueck DH, Shenkman EA
Stratified split sampling of electronic health records
Data extracted from electronic health records may require very different approaches for model building and analysis than data from clinical research. Because electronic health record data is designed for clinical use, researchers need to engage in the iterative process of defining and provide clear definitions of outcome and predictor variables and assessing associations. This process can increase Type I error rates and decrease the chance of replicability. Failure to consider subgroups may mask heterogeneous relationships between predictor and outcome by subgroups, thus decreasing the generalizability of the findings. To improve the likelihood of both replicability and generalizability, the researchers recommended utilizing a stratified split sample approach for studies using electronic health records. The researchers illustrate the approach through an electronic health record study of the relationships between socio-demographic factors and uptake of hepatic cancer screening, and potential heterogeneity of association in subgroups defined by gender, self-identified race and ethnicity, census-tract level poverty and insurance type.
AHRQ-funded; HS028283.
Citation: Huo T, Glueck DH, Shenkman EA .
Stratified split sampling of electronic health records
BMC Med Res Methodol 2023 May 25; 23(1):128. doi: 10.1186/s12874-023-01938-0..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Research Methodologies, Health Services Research (HSR)
Chu CD, Lenoir KM, Rai NK
Concordance between clinical outcomes in the systolic blood pressure intervention trial and in the electronic health record.
This study examined the role that electronic health records (EHRs) can play in follow-up for concordance with trial-ascertained outcomes. The authors linked EHR and trial data for participants in the Systolic Blood Pressure Intervention Trial (SPRINT), a randomized trial comparing intensive and standard blood pressure targets. Among participants with available EHR data concurrent to trial-ascertained outcomes, they calculated sensitivity, specificity, positive predictive value, and negative predictive value for EHR-recorded cardiovascular disease (CVD) events, using the gold standard of SPRINT-adjudicated outcomes (myocardial infarction (MI)/acute coronary syndrome (ACS), heart failure, stroke, and composite CVD events). They additionally compared the incidence of non-CVD adverse events (hyponatremia, hypernatremia, hypokalemia, hyperkalemia, bradycardia, and hypotension) in trial versus EHR data. Of the 2468 SPRINT participants included, EHR data demonstrated ≥80% sensitivity and specificity, and ≥99% negative predictive value for MI/ACS, heart failure, stroke, and composite CVD events. Positive predictive value ranged from 26% for heart failure to 52% for MI/ACS. Conclusions were that EHR data uniformly identified more non-CVD adverse events and higher incidence rates compared with trial ascertainment.
AHRQ-funded; HS026383.
Citation: Chu CD, Lenoir KM, Rai NK .
Concordance between clinical outcomes in the systolic blood pressure intervention trial and in the electronic health record.
Contemp Clin Trials 2023 May; 128:107172. doi: 10.1016/j.cct.2023.107172..
Keywords: Blood Pressure, Electronic Health Records (EHRs), Health Information Technology (HIT), Cardiovascular Conditions
Zhang J, Kummerfield E, Hultman G
Application of causal discovery algorithms in studying the nephrotoxicity of remdesivir using longitudinal data from the EHR.
Researchers analyzed the role of remdesivir in the mechanism and optimal treatment of the development of acute kidney injury (AKI) in the setting of COVID. Applying causal discovery machine learning techniques, they built multifactorial causal models of COVID-AKI; risk factors and renal function measures were represented in a temporal sequence using longitudinal data from Electronic Health Records. Their results indicated a need for assessment of renal function on second- and third-day use of remdesivir, and also showed that remdesivir may pose less risk to AKI than existing conditions of chronic kidney disease.
AHRQ-funded; HS024532.
Citation: Zhang J, Kummerfield E, Hultman G .
Application of causal discovery algorithms in studying the nephrotoxicity of remdesivir using longitudinal data from the EHR.
AMIA Annu Symp Proc 2023 Apr 29; 2022:1227-36..
Keywords: COVID-19, Electronic Health Records (EHRs), Health Information Technology (HIT), Medication, Adverse Drug Events (ADE), Adverse Events
Hobensack M, Song J, Chae S
Capturing concerns about patient deterioration in narrative documentation in home healthcare.
This study aimed to build machine learning algorithms to identify “concerning” narrative notes of home healthcare (HHC) patients and identify emergency themes to support early identification of patients at risk for deterioration. Six algorithms were applied to 4000 narrative notes from a HHC agency to classify notes as either "concerning" or "not concerning." Emerging themes were identified using Latent Dirichlet Allocation bag of words topic modeling. Emerging themes of concern included patient-clinician communication, HHC services provided, gait challenges, mobility concerns, wounds, and caregivers. Most of these themes had already been identified in previous literature as increasing risk for adverse events.
AHRQ-funded; HS027742.
Citation: Hobensack M, Song J, Chae S .
Capturing concerns about patient deterioration in narrative documentation in home healthcare.
AMIA Annu Symp Proc 2023 Apr 29; 2022:552-59..
Keywords: Home Healthcare, Electronic Health Records (EHRs), Health Information Technology (HIT)
Moy AJ, Cato KD, Withall J
Using time series clustering to segment and infer emergency department nursing shifts from electronic health record log files.
Clinical shifts are an essential unit of work recognized in clinical settings and may function as a primary unit of analysis in the study of documentation burden. The purpose of this proof- of-concept study was to investigate the feasibility of a new approach utilizing time series clustering to segment and infer clinician shifts from electronic health record (HER) log files. The researchers recorded 33,535,585 events between April-June 2021 and computationally identified 43,911 potential shifts among 2,285 emergency department nurses. On average, shifts were 10.6±3.1 hours in duration. Researchers classified the shifts based on type: day, evening, night; and length: 12-hour, 8-hour, other. The preliminary results of the study found that unsupervised clustering methods may be a feasible approach for quickly identifying clinician shifts.
AHRQ-funded; HS028454.
Citation: Moy AJ, Cato KD, Withall J .
Using time series clustering to segment and infer emergency department nursing shifts from electronic health record log files.
AMIA Annu Symp Proc 2023 Apr 29; 2022:805-14..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Emergency Department, Workforce
Kneifati-Hayek JZ, Applebaum JR, Schechter CB
Effect of restricting electronic health records on clinician efficiency: substudy of a randomized clinical trial.
This substudy of a prior randomized controlled trial compared clinician efficiency between electronic health records (EHRs) where the EHR limits the clinician to opening 1 record at a time to unrestricted configuration allowing up to 4 records open concurrently. Among a total of 2556 clinicians, there was no significant difference between unrestricted and restricted groups in total active minutes per day (115.1 vs 113.3 min, respectively), overall or by clinician type and practice area.
AHRQ-funded; HS026121; HS023704.
Citation: Kneifati-Hayek JZ, Applebaum JR, Schechter CB .
Effect of restricting electronic health records on clinician efficiency: substudy of a randomized clinical trial.
J Am Med Inform Assoc 2023 Apr 19; 30(5):953-57. doi: 10.1093/jamia/ocad025..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT)
Moy AJ, Hobensack M, Marshall K
Understanding the perceived role of electronic health records and workflow fragmentation on clinician documentation burden in emergency departments.
This study’s goal was to understand the perceived role of electronic health records (EHR) and workflow fragmentation on clinician documentation burden in the emergency department (ED). The authors conducted semistructured interviews with a national sample of US prescribing providers and registered nurses who actively practice in the adult ED setting and use Epic Systems' EHR. They recruited 12 prescribing providers and 12 registered nurses. Six themes were found related to EHR factors perceived to contribute to documentation burden including lack of advanced EHR capabilities, absence of EHR optimization for clinicians, poor user interface design, hindered communication, increased manual work, and added workflow blockages, and five themes associated with cognitive load. The relationship between workflow fragmentation and EHR documentation burden brought up two themes: underlying sources and adverse consequences.
AHRQ-funded; HS028454.
Citation: Moy AJ, Hobensack M, Marshall K .
Understanding the perceived role of electronic health records and workflow fragmentation on clinician documentation burden in emergency departments.
J Am Med Inform Assoc 2023 Apr 19; 30(5):797-808. doi: 10.1093/jamia/ocad038..
Keywords: Electronic Health Records (EHRs), Workflow, Health Information Technology (HIT), Emergency Department
Krevat SA, Samuel S, Boxley C
Identifying electronic health record contributions to diagnostic error in ambulatory settings through legal claims analysis.
The purpose of this study was to evaluate legal claims data to assess whether there is a relationship between problems with electronic health records and diagnostic errors. The researchers also explored specific types of errors that took place and at which point in the diagnostic process the errors occurred.
AHRQ-funded; HS027119.
Citation: Krevat SA, Samuel S, Boxley C .
Identifying electronic health record contributions to diagnostic error in ambulatory settings through legal claims analysis.
JAMA Netw Open 2023 Apr 3; 6(4):e238399. doi: 10.1001/jamanetworkopen.2023.8399..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality
Lane S, Fitzsimmons E, Zelefsky A
Assessing electronic health literacy at an urban academic hospital.
The purpose of this study was to explore electronic health literacy (EHL) in patients at an urban, academic hospital in the Bronx, and evaluate for relationships between EHL levels and different demographic variables. The researchers designed a cross-sectional, observational study in adults 18 years or more who presented for services at the Montefiore Einstein Center for Cancer Care (MECCC) Department of Radiation Oncology or the Montefiore Department of Medicine in the Bronx. The study evaluated EHL using the existing, validated eHealth Literacy Scale (eHEALS) survey and the researchers’ newly developed eHealth Literacy Objective Scale-Scenario Based (eHeLiOS-SB) tool. The study found that there was a statistically significant relationship between age and EHL as evaluated by both eHEALS and eHeLiOS-SB, with lower EHL scores for older adults. A specific question designed to evaluate attitudes toward digital health technologies revealed that the majority of participants held a positive attitude toward those types of applications.
AHRQ-funded; HS025645.
Citation: Lane S, Fitzsimmons E, Zelefsky A .
Assessing electronic health literacy at an urban academic hospital.
Appl Clin Inform 2023 Mar; 14(2):365-73. doi: 10.1055/a-2041-4500..
Keywords: Health Literacy, Electronic Health Records (EHRs), Telehealth, Health Information Technology (HIT)
Humbert-Droz M, Izadi Z, Schmajuk G
Development of a natural language processing system for extracting rheumatoid arthritis outcomes from clinical notes using the national rheumatology informatics system for effectiveness registry.
Researchers developed and evaluated a natural language processing pipeline for extracting outcome measures in rheumatology from free-text outpatient rheumatology notes within the ACR's Rheumatology Informatics System for Effectiveness (RISE) registry. All patients in RISE from 2015 to 2018 were included. The researchers found the pipeline to have good internal and external validity and they concluded that it could facilitate measurement of clinical and patient reported outcomes for use in both research and quality measurement.
AHRQ-funded; HS025638.
Citation: Humbert-Droz M, Izadi Z, Schmajuk G .
Development of a natural language processing system for extracting rheumatoid arthritis outcomes from clinical notes using the national rheumatology informatics system for effectiveness registry.
Arthritis Care Res 2023 Mar; 75(3):608-15. doi: 10.1002/acr.24869..
Keywords: Arthritis, Electronic Health Records (EHRs), Health Information Technology (HIT), Outcomes, Patient-Centered Outcomes Research, Evidence-Based Practice
Halliday TM, McFadden M, Cedillo M
Lifestyle strategies after intentional weight loss: results from the MAINTAIN-pc randomized trial.
The aim of this study was to explore the strategies related with successful long-term weight loss maintenance. Researchers analyzed data from the 24-month Maintaining Activity and Nutrition Through Technology-Assisted Innovation in Primary Care (MAINTAIN-pc) trial. MAINTAIN-pc recruited 194 adults with recent intentional weight loss and randomized participants a group using tracking tools plus coaching (i.e., coaching group) or tracking tools without coaching (i.e., tracking-only group). The participants reported the lifestyle strategies they utilized in the previous 6 months, including self-monitoring, group support, behavioral skills, and professional support. The study found that at baseline, 100% used behavioral skills, 73% used group support, 69% used self-monitoring, and 68% used professional support in the past 6 months; at 24 months, the rates were 98%, 60%, 75%, and 61%, respectively. The number of participants using individual strategies did not vary significantly over time, but the overall number of strategies participants reported decreased. A greater number of strategies were utilized at baseline and 6 months compared to 12- and 24-month follow-ups. The coaching group utilized a greater number of strategies at months 6 and 12 than the tracking-only group. Consistent utilization of professional support strategies over the 24-month study period was related with less weight regain.
AHRQ-funded; HS021162.
Citation: Halliday TM, McFadden M, Cedillo M .
Lifestyle strategies after intentional weight loss: results from the MAINTAIN-pc randomized trial.
Transl J Am Coll Sports Med 2023 Spring; 8(2). doi: 10.1249/tjx.0000000000000220..
Keywords: Lifestyle Changes, Obesity, Primary Care, Electronic Health Records (EHRs), Health Information Technology (HIT)
Harle CA, Wu W, Vest JR
Accuracy of electronic health record food insecurity, housing instability, and financial strain screening in adult primary care.
The objective of this study was to assess the accuracy of electronic health record–based multidomain screening questionnaires on social risk factors. Researchers used single-domain questionnaires on individual factors such as food insecurity, housing instability, and financial strain as external standards.
AHRQ-funded; HS028636.
Citation: Harle CA, Wu W, Vest JR .
Accuracy of electronic health record food insecurity, housing instability, and financial strain screening in adult primary care.
JAMA 2023 Feb 7; 329(5):423-24. doi: 10.1001/jama.2022.23631..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Primary Care, Screening, Social Determinants of Health
Coley RY, Smith JJ, Karliner L RY, Smith JJ, Karliner L
External validation of the eRADAR risk score for detecting undiagnosed dementia in two real-world healthcare systems.
Drupal date: Feb, 2023
It is estimated that half of the individuals with dementia remain undiagnosed. The electronic health record (EHR) Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) was designed to detect older adults at risk of undiagnosed dementia using routinely gathered clinical information. The purpose of this retrospective cohort study was to externally validate eRADAR in two real-world healthcare systems, examining its performance over time and across race/ethnicity. The study found a total of 7631 dementia diagnoses were observed at KPWA and 216 at UCSF. The area under the curve was 0.84 (95% confidence interval: 0.84-0.85) at KPWA and 0.79 (0.76-0.82) at UCSF. Using the 90th percentile as the cut point for identifying high-risk patients, sensitivity was 54% (53-56%) at KPWA and 44% (38-51%) at UCSF. Performance was consistent over time, including across the transition from International Classification of Diseases, version 9 (ICD-9) to ICD-10 codes, and across racial/ethnic groups (although small samples limited precision in some groups). The study concluded that eRADAR demonstrated strong external validity for identifying undiagnosed dementia in two healthcare systems with diverse patient populations and varying availability of external healthcare data for risk calculations. This study showed that eRADAR is generalizable from a research sample to real-world clinical populations, transportable across health systems, resilient to temporal changes in healthcare, and exhibits similar performance across major racial/ethnic groups.
It is estimated that half of the individuals with dementia remain undiagnosed. The electronic health record (EHR) Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) was designed to detect older adults at risk of undiagnosed dementia using routinely gathered clinical information. The purpose of this retrospective cohort study was to externally validate eRADAR in two real-world healthcare systems, examining its performance over time and across race/ethnicity. The study found a total of 7631 dementia diagnoses were observed at KPWA and 216 at UCSF. The area under the curve was 0.84 (95% confidence interval: 0.84-0.85) at KPWA and 0.79 (0.76-0.82) at UCSF. Using the 90th percentile as the cut point for identifying high-risk patients, sensitivity was 54% (53-56%) at KPWA and 44% (38-51%) at UCSF. Performance was consistent over time, including across the transition from International Classification of Diseases, version 9 (ICD-9) to ICD-10 codes, and across racial/ethnic groups (although small samples limited precision in some groups). The study concluded that eRADAR demonstrated strong external validity for identifying undiagnosed dementia in two healthcare systems with diverse patient populations and varying availability of external healthcare data for risk calculations. This study showed that eRADAR is generalizable from a research sample to real-world clinical populations, transportable across health systems, resilient to temporal changes in healthcare, and exhibits similar performance across major racial/ethnic groups.
AHRQ-funded; HS026369.
Citation: Coley RY, Smith JJ, Karliner L RY, Smith JJ, Karliner L .
External validation of the eRADAR risk score for detecting undiagnosed dementia in two real-world healthcare systems.
J Gen Intern Med 2023 Feb; 38(2):351-60. doi: 10.1007/s11606-022-07736-6..
Keywords: Dementia, Neurological Disorders, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT)
Hobensack M, Song J, Scharp D
Machine learning applied to electronic health record data in home healthcare: a scoping review.
This literature review aimed to synthesize and appraise the literature describing the application of machine learning to predict adverse outcomes (e.g., hospitalization, mortality) using electronic health record (EHR) data in the home healthcare (HHC) setting. The secondary aim was to evaluate the comprehensiveness of predictors used in the machine learning algorithms guided by the Biopsychosocial Model. Studies were included if they 1) described services provided in the HHC setting, 2) applied machine learning algorithms to predict adverse outcomes, defined as outcomes related to patient deterioration, 3) used EHR data and, 4) focused on the adult population. Predictors were mapped to the Biopsychosocial Model. The final sample included 20 studies, of which 18 used predictors from standardized assessments integrated in the EHR. The most common outcome was hospitalization (55%), followed by mortality (25%). About 35% of studies excluded psychological predictors. Most studies (75%) demonstrated high or unclear risk of bias with tree based algorithms most frequently applied (75%).
AHRQ-funded; HS027742.
Citation: Hobensack M, Song J, Scharp D .
Machine learning applied to electronic health record data in home healthcare: a scoping review.
Int J Med Inform 2023 Feb; 170:104978. doi: 10.1016/j.ijmedinf.2022.104978..
Keywords: Home Healthcare, Electronic Health Records (EHRs), Health Information Technology (HIT)
Kanbar LJ, Dexheimer Jw, Zahner J
Standardizing electronic health record ventilation data in the pediatric long-term mechanical ventilator-dependent population.
This research aimed to create a framework for standardizing mechanical ventilation terminology using ventilator data for a cohort of children who were weaned from mechanical ventilation (MV) to long-term mechanical ventilation (LTMV). Currently there is a lack of data standardization which is a major barrier to data sharing. The authors proposed a framework for standardizing the data using a common data model (CDM) across multiple populations and sites. They extracted and described relevant electronic health record (EHR) ventilation data. They developed a framework for Clinical Ideas into the PEDSnet CDM based on the Observational Medical Outcomes Partnership (OMOP). They identified 78 children with LMTV dependence who were weaned from ventilator support. They found 25 unique device names and 28 unique ventilation mode names used in the cohort. They decided on the following data concepts: device, interface, ventilation mode, settings, measurements, and duration of ventilation usage per day. They used Concepts from the SNOMED-CT vocabulary and integrated an existing ventilator mode taxonomy to create a framework for CDM and OMOP integration.
AHRQ-funded; HS026393.
Citation: Kanbar LJ, Dexheimer Jw, Zahner J .
Standardizing electronic health record ventilation data in the pediatric long-term mechanical ventilator-dependent population.
Pediatr Pulmonol 2023 Feb; 58(2):433-40. doi: 10.1002/ppul.26204..
Keywords: Children/Adolescents, Electronic Health Records (EHRs), Health Information Technology (HIT)
Apathy NC, Hare AJ, Fendrich S
I had not time to make it shorter: an exploratory analysis of how physicians reduce note length and time in notes.
The authors analyzed observed reductions in physicians’ note length and documentation time, both of which contribute to EHR burden and burnout. Their study used EHR metadata for ambulatory physician Epic users and examined changes in note composition of physicians who decreased note length and/or documentation time. Their findings showed that note length decreases were primarily attributable to reductions in copy/paste text and templated text, while note time decreases were primarily attributable to reductions in manual text. They concluded that future research should explore scalable burden-reduction initiatives that are responsive to both note bloat and documentation time.
AHRQ-funded; HS026116.
Citation: Apathy NC, Hare AJ, Fendrich S .
I had not time to make it shorter: an exploratory analysis of how physicians reduce note length and time in notes.
J Am Med Inform Assoc 2023 Jan18; 30(2):355-60. doi: 10.1093/jamia/ocac211..
Keywords: Provider: Physician, Burnout, Electronic Health Records (EHRs), Health Information Technology (HIT)
Cedillo M, Kukhareva PV, Larsen SM
Impact of electronic health record-coaching features in weight change: a secondary analysis from the MAINTAIN-pc randomized trial.
This study evaluated whether coaching features were successfully transmitted via electronic health record (EHR) communication and to evaluate their relationship with weight change in a previously tested EHR-based coaching intervention. The authors conducted a secondary analysis from the Maintaining Activity and Nutrition through Technology-Assisted Innovation in Primary Care (MAINTAIN-pc) study randomized clinical trial in nine primary care practices and one specialty practice (endocrinology) affiliated with the University of Pittsburgh Medical Center. Eligible participants were aged 18 to 75 years, had intentional 5% weight loss in the previous 2 years, had access to an internet-connected computer, and had receipt of care from a University of Pittsburgh Medical Center primary care provider. Participants content with intervention delivery via the EHR and those who felt a strong connection to their coach had significantly less weight regain. Participants who had needs unmet by the intervention (e.g., "in-person" support in a group setting or individual settings) regained more weight. The results suggest heterogeneity in the patient population regarding preference for in-person versus EHR-based coaching formats.
AHRQ-funded; HS021162.
Citation: Cedillo M, Kukhareva PV, Larsen SM .
Impact of electronic health record-coaching features in weight change: a secondary analysis from the MAINTAIN-pc randomized trial.
Obesity 2023 Jan;31(1):31-36. doi: 10.1002/oby.23595..
Keywords: Electronic Health Records (EHRs), Obesity, Obesity: Weight Management, Lifestyle Changes
Rule A, Melnick ER, Apathy NC
Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures.
The purpose of this study was to compare studies that utilize vendor-derived and investigator-derived measures of electronic health records (EHR) and to evaluate consistency across studies. The researchers reviewed PubMed for articles published between July 2019 and December 2021 that utilized measures of EHR use obtained from EHR event logs. The study found that 102 articles met the criteria for inclusion; of those, 40 utilized vendor-derived measures, 61 utilized investigator-derived measures, and 1 utilized both. Those studies utilizing vendor-derived measures had a greater likelihood of observing EHR use only in ambulatory settings and only by physicians or advanced practice providers compared with those employing investigator-derived measures. Studies utilizing vendor-derived measures also had a greater likelihood of measuring durations of EHR use, but definitions of measures (such as time outside scheduled hours) varied broadly. The researchers concluded that vendor-derived measures are being used more to study EHR use, but only by certain clinical roles. The amount of studies employing event logs to observe EHR use continues to increase, but with lack of consistency in measure definitions and significant differences between studies that utilize vendor-derived and investigator-derived methods.
AHRQ-funded; HS026116.
Citation: Rule A, Melnick ER, Apathy NC .
Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures.
J Am Med Inform Assoc 2022 Dec 13;30(1):144-55. doi: 10.1093/jamia/ocac177..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT)
Bell SK, Bourgeois F, Dong J
Patient identification of diagnostic safety blindspots and participation in "good catches" through shared visit notes.
The goal of this study was to investigate whether sharing clinical notes with patients supported identification of potential breakdowns in the diagnostic process that might be difficult for clinical staff to observe -- "diagnostic safety blindspots." Researchers analyzed patient-reported ambulatory documentation errors among patients at 3 U.S. healthcare centers. Older, female, unemployed, disabled, or sicker patients, or patients who worked in healthcare, were more likely to identify blindspots; patients who self-identified as Black, Asian, multiple races and those with less formal education as well as those who deferred decision-making to their providers were less likely to report blindspots. The researchers concluded that patients who read notes have unique insight about potential errors in their medical records and that organizations should encourage patient review of notes and create systems to track patient-reported blindspots.
AHRQ-funded; HS027367.
Citation: Bell SK, Bourgeois F, Dong J .
Patient identification of diagnostic safety blindspots and participation in "good catches" through shared visit notes.
Milbank Q 2022 Dec; 100(4):1121-65. doi: 10.1111/1468-0009.12593..
Keywords: Diagnostic Safety and Quality, Patient Safety, Electronic Health Records (EHRs), Health Information Technology (HIT)
Wu A, Huang RJ, Colón GR
Low rates of structured advance care planning documentation in electronic health records: results of a single-center observational study.
This study’s objective was to determine rates of structured advanced care planning (S-ACP) documentation in electronic health records (EHRs) using a single, large university medical center in California. This retrospective cohort study used records from all patients 65 and older with at least one ambulatory encounter at Stanford Health Care between 2012 and 2020, and without concurrent hospice care. Analysis of 187,316 unique outpatient encounters between 2012 and 2020 showed only 7,902 (4.2%) contained S-ACP documentation in the EHR. The most common methods of S-ACP documentation were through problem list diagnoses (40.3%) and scanned documents (40.0%). Senior Care (46.6%) and Palliative Care (25%) demonstrated the highest rates at the clinical level.
AHRQ-funded; HS028747.
Citation: Wu A, Huang RJ, Colón GR .
Low rates of structured advance care planning documentation in electronic health records: results of a single-center observational study.
BMC Palliat Care 2022 Nov 22;21(1):203. doi: 10.1186/s12904-022-01099-9..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT)
MacEwan SR, Sieck CJ, McAlearney AS
Geographic location impacts patient portal use via desktop and mobile devices.
The purpose of this study was to assess patient portal use by geographic location according to: proximity to the medical center offering the portal, urban/rural classification, and degree of digital distress. The study found that patients living further from the medical center, in rural areas, or in areas of higher digital distress were less likely to be active portal users. Patients living in areas of higher digital distress were more likely to use the mobile portal application instead of the desktop portal website. Users of the mobile portal application used portal functions more frequently, and being a mobile user had a greater impact on the use of some portal functions by patients residing in areas of higher digital distress. The researchers concluded that mobile patient portal applications have the potential to increase the use of patient portals.
AHRQ-funded; HS024091; HS024379.
Citation: MacEwan SR, Sieck CJ, McAlearney AS .
Geographic location impacts patient portal use via desktop and mobile devices.
J Med Syst 2022 Nov 16;46(12):97. doi: 10.1007/s10916-022-01881-5..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT)
Gupta AK, Kasthurirathne SN, Xu H
A framework for a consistent and reproducible evaluation of manual review for patient matching algorithms.
The authors proposed a robust framework for creating and evaluating manually reviewed gold standard data sets for measuring the performance of patient matching algorithms. They indicated that their framework can help record linkage method developers provide necessary transparency when creating and validating gold standard reference matching data sets. They concluded that this transparency will support both the internal and external validity of recording linkage studies and improve the robustness of new record linkage strategies.
AHRQ-funded; HS023808.
Citation: Gupta AK, Kasthurirathne SN, Xu H .
A framework for a consistent and reproducible evaluation of manual review for patient matching algorithms.
J Am Med Inform Assoc 2022 Nov 14;29(12):2105-09. doi: 10.1093/jamia/ocac175..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT)
Sittig DF, Sherman JD, Eckelman MJ
i-CLIMATE: a "clinical climate informatics" action framework to reduce environmental pollution from healthcare.
This article describes an action framework called “Information technology-enabled Clinical cLimate InforMAtics acTions for the Environment” (i-CLIMATE) to reduce environmental pollution from healthcare. The framework has 5 actionable components: (1) create a circular economy for health IT, (2) reduce energy consumption through smarter use of health IT, (3) support more environmentally friendly decision-making by clinicians and health administrators, (4) mobilize healthcare workforce environmental stewardship through informatics, and (5) inform policies and regulations for change.
AHRQ-funded; HS027363.
Citation: Sittig DF, Sherman JD, Eckelman MJ .
i-CLIMATE: a "clinical climate informatics" action framework to reduce environmental pollution from healthcare.
J Am Med Inform Assoc 2022 Nov 14;29(12):2153-60. doi: 10.1093/jamia/ocac137..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT)
Ganeshan S, Pierce L, Mourad M
Impact of patient portal-based self-scheduling of diagnostic imaging studies on health disparities.
The purpose of this study was to explore the impact of self-scheduling on equitable access to care. The researchers utilized an electronic health record patient portal at the University of California San Francisco which deployed a self-scheduling tool allowing patients to self-schedule diagnostic imaging studies. The study found that among all patient portal users, Latinx, Black/African American, and non-English speaking patients, as well as patients with Medi-Cal, California's Medicaid program, and Medicare insurance were less likely to self-schedule studies. were all less likely to self-schedule when compared with commercially insured patients.
AHRQ-funded; HS026383.
Citation: Ganeshan S, Pierce L, Mourad M .
Impact of patient portal-based self-scheduling of diagnostic imaging studies on health disparities.
J Am Med Inform Assoc 2022 Nov 14;29(12):2096-100. doi: 10.1093/jamia/ocac152..
Keywords: Disparities, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT)
Kang D, Charlton P, Applebury DE
Utilizing eye tracking to assess electronic health record use by pharmacists in the intensive care unit.
The authors conducted a study using high-fidelity electronic health record (EHR)-based simulations with incorporated eye tracking to understand the workflow of critical care pharmacists within the EHR, with specific attention to the data elements most frequently viewed. They found that, in addition to medication information, laboratory data and clinical notes are key focuses of intensive care unit pharmacist review of patient records and that navigation to multiple screens is required in order to view these data with the EHR.
AHRQ-funded; HS023793.
Citation: Kang D, Charlton P, Applebury DE .
Utilizing eye tracking to assess electronic health record use by pharmacists in the intensive care unit.
Am J Health Syst Pharm 2022 Nov 7;79(22):2018-25. doi: 10.1093/ajhp/zxac158..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Intensive Care Unit (ICU), Critical Care, Provider: Pharmacist