scholarly journals Does an interactive trust-enhanced electronic consent improve patient experiences when asked to share their health records for research? A randomized trial

2019 ◽  
Vol 26 (7) ◽  
pp. 620-629 ◽  
Author(s):  
Christopher A Harle ◽  
Elizabeth H Golembiewski ◽  
Kiarash P Rahmanian ◽  
Babette Brumback ◽  
Janice L Krieger ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Research Process ◽  
Future Research ◽  
Health Record ◽  
Primary Care Visit ◽  
Health Records ◽  
Patient Reported ◽  
Medical Researchers ◽  
Record Data ◽  
Lower Education

Abstract Objective In the context of patient broad consent for future research uses of their identifiable health record data, we compare the effectiveness of interactive trust-enhanced e-consent, interactive-only e-consent, and standard e-consent (no interactivity, no trust enhancement). Materials and Methods A randomized trial was conducted involving adult participants making a scheduled primary care visit. Participants were randomized into 1 of the 3 e-consent conditions. Primary outcomes were patient-reported satisfaction with and subjective understanding of the e-consent. Secondary outcomes were objective knowledge, perceived voluntariness, trust in medical researchers, consent decision, and time spent using the application. Outcomes were assessed immediately after use of the e-consent and at 1-week follow-up. Results Across all conditions, participants (N = 734) reported moderate-to-high satisfaction with consent (mean 4.3 of 5) and subjective understanding (79.1 of 100). Over 94% agreed to share their health record data. No statistically significant differences in outcomes were observed between conditions. Irrespective of condition, black participants and those with lower education reported lower satisfaction, subjective understanding, knowledge, perceived voluntariness, and trust in medical researchers, as well as spent more time consenting. Conclusions A large majority of patients were willing to share their identifiable health records for research, and they reported positive consent experiences. However, incorporating optional additional information and messages designed to enhance trust in the research process did not improve consent experiences. To improve poorer consent experiences of racial and ethnic minority participants and those with lower education, other novel consent technologies and processes may be valuable. (An Interactive Patient-Centered Consent for Research Using Medical Records; NCT03063268)

scholarly journals From screening to ascertainment of the primary outcome using electronic health records: Challenges in the STRIDE trial

2020 ◽  
Vol 17 (4) ◽  
pp. 346-350
Author(s):  
Denise Esserman
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Care Systems ◽  
Electronic Health ◽  
Data Elements

Electronic health record data are a rich resource and can be utilized to answer a wealth of research questions. It is important when using electronic health record data in clinical trials that systems be put in place and vetted prior to enrollment to ensure data elements can be collected consistently across all health care systems. It is often overlooked how something conceptualized on paper (e.g. use of the electronic health record in a study) can be difficult to implement in practice. This article discusses some of the challenges in using electronic health records in the conduct of the STRIDE (Strategies to Reduce Injuries and Develop Confidence in Elders) trial, how we handled those challenges, and the lessons we learned for the conduct of future trials looking to employ the electronic health record.

scholarly journals Enhanced Screening and Research Data Collection via Automated EHR Data Capture and Early Identification of Sepsis

2019 ◽  
Vol 5 ◽  
pp. 237796081985097
Author(s):  
Reba Umberger ◽  
Chayawat “Yo” Indranoi ◽  
Melanie Simpson ◽  
Rose Jensen ◽  
James Shamiyeh ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Data Extraction ◽  
Future Research ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Cross Sectional ◽  
Apply Research ◽  
Record Data ◽  
Electronic Health ◽  
Research Criteria

Clinical research in sepsis patients often requires gathering large amounts of longitudinal information. The electronic health record can be used to identify patients with sepsis, improve participant study recruitment, and extract data. The process of extracting data in a reliable and usable format is challenging, despite standard programming language. The aims of this project were to explore infrastructures for capturing electronic health record data and to apply criteria for identifying patients with sepsis. We conducted a prospective feasibility study to locate and capture/abstract electronic health record data for future sepsis studies. We located parameters as displayed to providers within the system and then captured data transmitted in Health Level Seven® interfaces between electronic health record systems into a prototype database. We evaluated our ability to successfully identify patients admitted with sepsis in the target intensive care unit (ICU) at two cross-sectional time points and then over a 2-month period. A majority of the selected parameters were accessible using an iterative process to locate and abstract them to the prototype database. We successfully identified patients admitted to a 20-bed ICU with sepsis using four data interfaces. Retrospectively applying similar criteria to data captured for 319 patients admitted to ICU over a 2-month period was less sensitive in identifying patients admitted directly to the ICU with sepsis. Classification into three admission categories (sepsis, no-sepsis, and other) was fair (Kappa .39) when compared with manual chart review. This project confirms reported barriers in data extraction. Data can be abstracted for future research, although more work is needed to refine and create customizable reports. We recommend that researchers engage their information technology department to electronically apply research criteria for improved research screening at the point of ICU admission. Using clinical electronic health records data to classify patients with sepsis over time is complex and challenging.

scholarly journals Alert dwell time: introduction of a measure to evaluate interruptive clinical decision support alerts

2015 ◽  
Vol 23 (e1) ◽  
pp. e138-e141 ◽  
Author(s):  
Robert B McDaniel ◽  
Jonathan D Burlison ◽  
Donald K Baker ◽  
Murad Hasan ◽  
Jennifer Robertson ◽  
...  
Keyword(s):  
Dwell Time ◽  
Clinical Decision ◽  
Future Research ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Log Files ◽  
Resident Physicians ◽  
Record Data ◽  
Effectiveness And Efficiency ◽  
Alerting Systems

Abstract Metrics for evaluating interruptive prescribing alerts have many limitations. Additional methods are needed to identify opportunities to improve alerting systems and prevent alert fatigue. In this study, the authors determined whether alert dwell time—the time elapsed from when an interruptive alert is generated to when it is dismissed—could be calculated by using historical alert data from log files. Drug–drug interaction (DDI) alerts from 3 years of electronic health record data were queried. Alert dwell time was calculated for 25,965 alerts, including 777 unique DDIs. The median alert dwell time was 8 s (range, 1–4913 s). Resident physicians had longer median alert dwell times than other prescribers ( P <  .001). The 10 most frequent DDI alerts ( n =  8759 alerts) had shorter median dwell times than alerts that only occurred once ( P <  .001). This metric can be used in future research to evaluate the effectiveness and efficiency of interruptive prescribing alerts.

scholarly journals Challenges associated with missing data in electronic health records: A case study of a risk prediction model for diabetes using data from Slovenian primary care

2017 ◽  
Vol 25 (3) ◽  
pp. 951-959 ◽  
Author(s):  
Gregor Stiglic ◽  
Primoz Kocbek ◽  
Nino Fijacko ◽  
Aziz Sheikh ◽  
Majda Pajnkihar
Keyword(s):  
Missing Data ◽  
Electronic Health Records ◽  
Prediction Model ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Electronic Health

The increasing availability of data stored in electronic health records brings substantial opportunities for advancing patient care and population health. This is, however, fundamentally dependant on the completeness and quality of data in these electronic health records. We sought to use electronic health record data to populate a risk prediction model for identifying patients with undiagnosed type 2 diabetes mellitus. We, however, found substantial (up to 90%) amounts of missing data in some healthcare centres. Attempts at imputing for these missing data or using reduced dataset by removing incomplete records resulted in a major deterioration in the performance of the prediction model. This case study illustrates the substantial wasted opportunities resulting from incomplete records by simulation of missing and incomplete records in predictive modelling process. Government and professional bodies need to prioritise efforts to address these data shortcomings in order to ensure that electronic health record data are maximally exploited for patient and population benefit.

scholarly journals New AI Technologies to Enrich Electronic Health Record Data Sets With Self-Report Scores in Geriatrics

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 275-275
Author(s):  
Ricardo Pietrobon
Keyword(s):  
Electronic Health Record ◽  
Self Report ◽  
Health Record ◽  
Data Sets ◽  
Electronic Health Record Data ◽  
Patient Reported ◽  
Record Data ◽  
Data Source ◽  
Electronic Health ◽  
Rich Data

Abstract Although electronic health record data present a rich data source for health service researchers, for the most part, they lack self-report information. Although recent CMS projects have provided hospitals with incentives to collect patient-reported outcomes for select procedures, the process often leads to a substantial percentage of missing data, also being expensive as it requires the assistance of research coordinators. In this presentation, we will cover Artificial Intelligence-based based technologies to reduce the burden of data collection, allowing for its expansion across clinics and conditions. The technology involves the use of algorithms to predict self-report scores based on widely available claims data. Following previous work predicting frailty scores from existing variables, we expand its use with scores related to quality of life, i.e. mental health and physical function, and cognition. Accuracy metrics are presented both in cross-validation as well as external samples.

scholarly journals Dealing with Un(Expected) Ethical Dilemma: Experience from the Field

2018 ◽  
Author(s):  
Zaleha Othman ◽  
Fathilatul Zakimi Abdul Hamid
Keyword(s):  
Qualitative Research ◽  
Ethical Dilemma ◽  
Ethical Issues ◽  
Ethical Dilemmas ◽  
Research Integrity ◽  
Data Interpretation ◽  
Research Process ◽  
Future Research ◽  
Sensitive Topics ◽  
Record Data

Despite the growing interest in qualitative research and discussion of ethics, there has been little focus in the literature on the specific ethical dilemmas faced by researchers. In this paper, we share our fieldwork experiences regarding the ethical dilemmas that we encountered while doing research on a sensitive topic. Specifically, we share some of the ethical dilemmas, that is, concerning confidentiality, anonymity, legitimacy, controversial data, interpretation and off-the-record data, which emerged from the research. Most importantly, this paper shares ideas concerning how researchers might deal with ethical issues while preserving their integrity in the research process. Overall, this paper suggests approaches that qualitative researchers can adopt when doing research on sensitive topics. The paper contributes towards closing an existing gap in the literature, making visible the challenges frequently faced by qualitative researchers, that is, the vulnerability of researchers while preserving research integrity. Finally, this paper concludes with the suggestion that ethical dilemmas are part of the research process in doing qualitative research. However, it is suggested that future research should focus on ethical issues from the perspective of the researchers as well as the respondents.

The APPROACH trial: Assessing pain, patient-reported outcomes, and complementary and integrative health

2020 ◽  
Vol 17 (4) ◽  
pp. 351-359
Author(s):  
Steven B Zeliadt ◽  
Scott Coggeshall ◽  
Eva Thomas ◽  
Hannah Gelman ◽  
Stephanie L Taylor
Keyword(s):  
Electronic Health Record ◽  
Real Time ◽  
Patient Reported Outcomes ◽  
Health Record ◽  
Business Practices ◽  
Electronic Health Record Data ◽  
Integrative Health ◽  
Patient Reported ◽  
Record Data ◽  
Electronic Health

Electronic health record data can be used in multiple ways to facilitate real-world pragmatic studies. Electronic health record data can provide detailed information about utilization of treatment options to help identify appropriate comparison groups, access historical clinical characteristics of participants, and facilitate measuring longitudinal outcomes for the treatments being studied. An additional novel use of electronic health record data is to assess and understand referral pathways and other business practices that encourage or discourage patients from using different types of care. We describe an ongoing study utilizing access to real-time electronic health record data about changing patterns of complementary and integrative health services to demonstrate how electronic health record data can provide the foundation for a pragmatic study when randomization is not feasible. Conducting explanatory trials of the value of emerging therapies within a healthcare system poses ethical and pragmatic challenges, such as withholding access to specific services that are becoming widely available to patients. We describe how prospective examination of real-time electronic health record data can be used to construct and understand business practices as potential surrogates for direct randomization through an instrumental variables analytic approach. In this context, an example of a business practice is the internal hiring of acupuncturists who also provide yoga or Tai Chi classes and can offer these classes without additional cost compared to community acupuncturists. Here, the business practice of hiring internal acupuncturists is likely to encourage much higher rates of combined complementary and integrative health use compared to community referrals. We highlight the tradeoff in efficiency of this pragmatic approach and describe use of simulations to estimate the potential sample sizes needed for a variety of instrument strengths. While real-time monitoring of business practices from electronic health records provides insights into the validity of key independence assumptions associated with the instrumental variable approaches, we note that there may be some residual confounding by indication or selection bias and describe how alternative sources of electronic health record data can be used to assess the robustness of instrumental variable assumptions to address these challenges. Finally, we also highlight that while some clinical outcomes can be obtained directly from the electronic health record, such as longitudinal opioid utilization and pain intensity levels for the study of the value of complementary and integrative health, it is often critical to supplement clinical electronic health record–based measures with patient-reported outcomes. The experience of this example in evaluating complementary and integrative health demonstrates the use of electronic health record data in several novel ways that may be of use for designing future pragmatic trials.

scholarly journals What Every Reader Should Know About Studies Using Electronic Health Record Data But Is Afraid To Ask (Preprint)

2020 ◽  
Author(s):  
Isaac S Kohane ◽  
Bruce J Aronow ◽  
Paul Avillach ◽  
Brett K Beaulieu-Jones ◽  
Riccardo Bellazzi ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Real World ◽  
Warning Signs ◽  
Health Record ◽  
Real World Data ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Electronic Health

UNSTRUCTURED Coincident with the tsunami of Covid19-related manuscripts, there has been a surge of studies using Real World Data (RWD), including those obtained from electronic health records. Unfortunately, several of these studies have resulted in withdrawn publication because of concerns regarding their soundness and quality. We argue here that there are pre-analytic hints and warning signs that are useful in judging RWD studies that might otherwise pass statistical muster. We outline several of these signs and suggest that review of RWD manuscripts include those who are familiar with how such data are generated.

Leveraging electronic health records for predictive modeling of post-surgical complications

2017 ◽  
Vol 27 (11) ◽  
pp. 3271-3285 ◽  
Author(s):  
Grant B Weller ◽  
Jenna Lovely ◽  
David W Larson ◽  
Berton A Earnshaw ◽  
Marianne Huebner
Keyword(s):  
Electronic Health Record ◽  
Predictive Modeling ◽  
Surgical Complications ◽  
Decision Rules ◽  
Clinical Decision ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Electronic Health

Hospital-specific electronic health record systems are used to inform clinical practice about best practices and quality improvements. Many surgical centers have developed deterministic clinical decision rules to discover adverse events (e.g. postoperative complications) using electronic health record data. However, these data provide opportunities to use probabilistic methods for early prediction of adverse health events, which may be more informative than deterministic algorithms. Electronic health record data from a set of 9598 colorectal surgery cases from 2010 to 2014 were used to predict the occurrence of selected complications including surgical site infection, ileus, and bleeding. Consistent with previous studies, we find a high rate of missing values for both covariates and complication information (4–90%). Several machine learning classification methods are trained on an 80% random sample of cases and tested on a remaining holdout set. Predictive performance varies by complication, although an area under the receiver operating characteristic curve as high as 0.86 on testing data was achieved for bleeding complications, and accuracy for all complications compares favorably to existing clinical decision rules. Our results confirm that electronic health records provide opportunities for improved risk prediction of surgical complications; however, consideration of data quality and consistency standards is an important step in predictive modeling with such data.

Projecting a European Diploma in Psychology

2002 ◽  
Vol 7 (3) ◽  
pp. 221-224 ◽  
Author(s):  
Bernhard Wilpert
Keyword(s):  
Research Process ◽  
Project Team ◽  
Future Research ◽  
Research Results ◽  
External Constraints ◽  
Internal Procedures ◽  
The Eu

The paper presents an inside evaluation of the EuroPsyT project, funded by the EU Leonardo Program in 1999-2001. While standard research usually neglects to reflect on the internal and external constraints and opportunities under which research results are achieved, the paper stresses exactly those aspects: starting from a brief description of the overall objectives of the 11 countries project, the paper proceeds to describe the macro-context and the internal strengths and weaknesses of the project team, the internal procedures of cooperation,. and obstacles encountered during the research process. It winds up in noting some of the project's achievements and with a look towards future research.

Sign in / Sign up

Export Citation Format

Share Document