Electronic health records to facilitate clinical research

Electronic health records (EHRs) provide opportunities to enhance patient care, embed performance measures in clinical practice, and facilitate clinical research. Concerns have been raised about the increasing recruitment challenges in trials, burdensome and obtrusive data collection, and uncertain generalizability of the results. Leveraging electronic health records to counterbalance these trends is an area of intense interest. The initial applications of electronic health records, as the primary data source is envisioned for observational studies, embedded pragmatic or post-marketing registry-based randomized studies, or comparative effectiveness studies. Advancing this approach to randomized clinical trials, electronic health records may potentially be used to assess study feasibility, to facilitate patient recruitment, and streamline data collection at baseline and follow-up. Ensuring data security and privacy, overcoming the challenges associated with linking diverse systems and maintaining infrastructure for repeat use of high quality data, are some of the challenges associated with using electronic health records in clinical research. Collaboration between academia, industry, regulatory bodies, policy makers, patients, and electronic health record vendors is critical for the greater use of electronic health records in clinical research. This manuscript identifies the key steps required to advance the role of electronic health records in cardiovascular clinical research

Use of electronic medical records and biomarkers to manage risk and resource efficiencies

Peer reviewedPublisher PD

Systematic Multi-Domain Alzheimer's Risk Reduction Trial (SMARRT): Study Protocol.

This article describes the protocol for the Systematic Multi-domain Alzheimer's Risk Reduction Trial (SMARRT), a single-blind randomized pilot trial to test a personalized, pragmatic, multi-domain Alzheimer's disease (AD) risk reduction intervention in a US integrated healthcare delivery system. Study participants will be 200 higher-risk older adults (age 70-89 years with subjective cognitive complaints, low normal performance on cognitive screen, and ≥ two modifiable risk factors targeted by our intervention) who will be recruited from selected primary care clinics of Kaiser Permanente Washington, oversampling people with non-white race or Hispanic ethnicity. Study participants will be randomly assigned to a two-year Alzheimer's risk reduction intervention (SMARRT) or a Health Education (HE) control. Randomization will be stratified by clinic, race/ethnicity (non-Hispanic white versus non-white or Hispanic), and age (70-79, 80-89). Participants randomized to the SMARRT group will work with a behavioral coach and nurse to develop a personalized plan related to their risk factors (poorly controlled hypertension, diabetes with evidence of hyper or hypoglycemia, depressive symptoms, poor sleep quality, contraindicated medications, physical inactivity, low cognitive stimulation, social isolation, poor diet, smoking). Participants in the HE control group will be mailed general health education information about these risk factors for AD. The primary outcome is two-year cognitive change on a cognitive test composite score. Secondary outcomes include: 1) improvement in targeted risk factors, 2) individual cognitive domain composite scores, 3) physical performance, 4) functional ability, 5) quality of life, and 6) incidence of mild cognitive impairment, AD, and dementia. Primary and secondary outcomes will be assessed in both groups at baseline and 6, 12, 18, and 24 months

The European Institute for Innovation through Health Data

The European Institute for Innovation through Health Data (i~HD, www.i-hd.eu) has been formed as one of the key sustainable entities arising from the Electronic Health Records for Clinical Research (IMI-JU-115189) and SemanticHealthNet (FP7-288408) projects, in collaboration with several other European projects and initiatives supported by the European Commission. i~HD is a European not-for-profit body, registered in Belgium through Royal Assent. i~HD has been established to tackle areas of challenge in the successful scaling up of innovations that critically rely on high-quality and interoperable health data. It will specifically address obstacles and opportunities to using health data by collating, developing, and promoting best practices in information governance and in semantic interoperability. It will help to sustain and propagate the results of health information and communication technology (ICT) research that enables better use of health data, assessing and optimizing their novel value wherever possible. i~HD has been formed after wide consultation and engagement of many stakeholders to develop methods, solutions, and services that can help to maximize the value obtained by all stakeholders from health data. It will support innovations in health maintenance, health care delivery, and knowledge discovery while ensuring compliance with all legal prerequisites, especially regarding the insurance of patient's privacy protection. It is bringing multiple stakeholder groups together so as to ensure that future solutions serve their collective needs and can be readily adopted affordably and at scale

Clinical Practice Implementation to Address ASCVD Risk: A Practice Change in Primary Care

Practice Problem: Heart disease stands as the leading cause of mortality in the United States. While healthcare providers strive to identify and optimize prevention strategies, particularly in high-risk patient populations, notable gaps in care persist, notably in the management of modifiable risk factors such as low-density lipoprotein cholesterol (LDL). By harnessing the power of artificial intelligence (AI) integrated software within clinical settings, we can revolutionize the landscape of this devastating chronic disease. PICOT: The PICOT question that guided this project was: In Primary Care Advanced Practice Providers (APP) caring for high-risk and/or very high-risk patients with atherosclerotic cardiovascular disease (ASCVD) (P), how do automated electronic alerts with guideline-based recommendations (I) compare to standard notification practice (C) affect referral initiation to cardiology or prompt medication change (O) within 10 weeks (T)? Evidence: In the realm of modern healthcare, it is crucial to recognize the impact of AI on Electronic Health Records (EHRs). This fusion of data analysis and health information technology provides an opportunity for healthcare treatments to become much more effective, resulting in better patient outcomes. Fifteen studies that matched the inclusion criteria were collected and used as substantiating evidence for this project. Intervention: AI software integrated into the EHR system computed comprehensive data analytics, consequently discovering a substantial cohort of patients with an elevated risk profile for ASCVD, accompanied by an LDL-C level that exceeded established clinical guidelines. Subsequently, an automated communication was sent to the APP, furnishing them with pertinent notifications and offering referral recommendations. Outcome: By integrating AI processes into the EHR, data management is streamlined and real-time disease prevention analysis is achieved. The primary goal was to identify high-risk ASCVD patient groups using AI within the EHR and assess the effectiveness of AI-generated electronic alerts with clinical guidance in encouraging behavior change. The clinical significance of this data collection and implementation was substantial. While the statistical analysis produced relevant metrics, it also exhibited applicability in the clinical context. The data exposed a patient population lacking aggressive medical management or referrals, a concern noted by APPs. Conclusion: Introducing AI-based tools can direct the pathway of care and bridge crucial gaps in care in high-risk populations. The result of this technology utilization and integration offers timely screening strategies, education, clinical decision support, and opportunities to address vital pathways for providers and health systems to address ASCVD treatment gaps

Supporting Clinical Decision Making in Cancer Care Delivery

Background: Cancer treatment and management require complicated clinical decision making to provide the highest quality of care for an individual patient. This is facilitated in part with ever-increasing availability of medications and treatments but hindered due to barriers such as access to care, cost of medications, clinician knowledge, and patient preferences or clinical factors. Although guidelines for cancer treatment and many symptoms have been developed to inform clinical practice, implementation of these guidelines into practice is often delayed or does not occur. Informatics-based approaches, such as clinical decision support, may be an effective tool to improve guideline implementation by delivering patient-specific and evidence-based knowledge to the clinician at the point of care to allow shared decision making with a patient and their family. The large amount of data in the electronic health record can be utilized to develop, evaluate, and implement automated approaches; however, the quality of the data must first be examined and evaluated. Methods: This dissertation addresses gaps the literature about clinical decision making for cancer care delivery. Specifically, following an introduction and review of the literature for relevant topics to this dissertation, the researcher presents three studies. In Study One, the researcher explores the use of clinical decision support in cancer therapeutic decision making by conducting a systematic review of the literature. In Study Two, the researcher conducts a quantitative study to describe the rate of guideline concordant care provided for prevention of acute chemotherapy-induced nausea and vomiting (CINV) and to identify predictors of receiving guideline concordant care. In Study Three, the researcher conducts a mixed-methods study to evaluate the completeness, concordance, and heterogeneity of clinician documentation of CINV. The final chapter of this dissertation is comprised of key findings of each study, the strengths and limitations, clinical and research implications, and future research. Results: In Study One, the systematic review, the researcher identified ten studies that prospectively studied clinical decision support systems or tools in a cancer setting to guide therapeutic decision making. There was variability in these studies, including study design, outcomes measured, and results. There was a trend toward benefit, both in process and patient-specific outcomes. Importantly, few studies were integrated into the electronic health record. In Study Two, of 180 patients age 26 years or less, 36% received guideline concordant care as defined by pediatric or adult guidelines, as appropriate. Factors associated with receiving guideline concordant care included receiving a cisplatin-based regimen, being treated in adult oncology compared to pediatric oncology, and solid tumor diagnosis. In Study Three, of the 127 patient records reviewed for the documentation of chemotherapy-induced nausea and vomiting, 75% had prescriber assessment documented and 58% had nursing assessment documented. Of those who had documented assessments by both prescriber and nurse, 72% were in agreement of the presence/absence of chemotherapy-induced nausea and vomiting. After mapping the concept through the United Medical Language System and developing a post-coordinated expression to identify chemotherapy-induced nausea and vomiting in the text, 85% of prescriber documentation and 100% of nurse documentation could be correctly categorized as present/absent. Further descriptors of the symptoms, such as severity or temporality, however, were infrequently reported. Conclusion: In summary, this dissertation provides new knowledge about decision making in cancer care delivery. Specifically, in Study One the researcher describes that clinical decision support, one potential implementation strategy to improve guideline concordant care, is understudied or under published but a promising potential intervention. In Study Two, I identified factors that were associated with receipt of guideline concordant care for CINV, and these should be further explored to develop interventions. Finally, in Study Three, I report on the limitations of the data quality of CINV documentation in the electronic health record. Future work should focus on validating these results on a multi-institutional level

The Use of Routinely Collected Data in Clinical Trial Research

RCTs are the gold standard for assessing the effects of medical interventions, but they also pose many challenges, including the often-high costs in conducting them and a potential lack of generalizability of their findings. The recent increase in the availability of so called routinely collected data (RCD) sources has led to great interest in their application to support RCTs in an effort to increase the efficiency of conducting clinical trials. We define all RCTs augmented by RCD in any form as RCD-RCTs. A major subset of RCD-RCTs are performed at the point of care using electronic health records (EHRs) and are referred to as point-of-care research (POC-R). RCD-RCTs offer several advantages over traditional trials regarding patient recruitment and data collection, and beyond. Using highly standardized EHR and registry data allows to assess patient characteristics for trial eligibility and to examine treatment effects through routinely collected endpoints or by linkage to other data sources like mortality registries. Thus, RCD can be used to augment traditional RCTs by providing a sampling framework for patient recruitment and by directly measuring patient relevant outcomes. The result of these efforts is the generation of real-world evidence (RWE). Nevertheless, the utilization of RCD in clinical research brings novel methodological challenges, and issues related to data quality are frequently discussed, which need to be considered for RCD-RCTs. Some of the limitations surrounding RCD use in RCTs relate to data quality, data availability, ethical and informed consent challenges, and lack of endpoint adjudication which may all lead to uncertainties in the validity of their results. The purpose of this thesis is to help fill the aforementioned research gaps in RCD-RCTs, encompassing tasks such as assessing their current application in clinical research and evaluating the methodological and technical challenges in performing them. Furthermore, it aims to assess the reporting quality of published reports on RCD-RCTs