Obsessive-compulsive symptoms relating to psychosocial functioning for people with schizophrenia, schizoaffective disorder, or bipolar disorder

To assess the psychosocial functioning concerning obsessive-compulsive symptoms (OCS) and/or obsessive-compulsive disorder (OCD) comorbidity in people with schizophrenia, schizoaffective disorder, or bipolar disorder diagnosed in a large case register database in Southeast London. Data were retrieved from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) register using Clinical Record Interactive Search (CRIS) system, a platform allowing research on full but de-identified electronic health records for secondary and tertiary mental healthcare services. Information of schizophrenia, schizoaffective disorder, bipolar disorder diagnosis and OCS/OCD status was ascertained from structural or free-text fields through natural language processing (NLP) algorithms based on artificial intelligence techniques during the observation window of January 2007 to December 2016. Associations between comorbid OCS/OCD and recorded Health of the Nation Outcome Scales (HoNOS) for problems with activities of daily living (ADLs), living conditions, occupational and recreational activities, and relationships were estimated by logistic regression with socio-demographic confounders controlled. Of 15,412 subjects diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder, 2,358 (15.3%) experienced OCS without OCD, and 2,586 (16.8%) had OCD recorded. The presence of OCS/OCD was associated with more problems with relationships (adj.OR = 1.34, 95% CI: 1.25-1.44), ADLs (adj.OR = 1.31, 95%CI: 1.22-1.41), and living conditions (adj.OR = 1.31, 95% CI: 1.22-1.41). Sensitivity analysis revealed similar outcomes. Comorbid OCS/OCD was associated with poorer psychosocial functioning in people with schizophrenia, schizoaffective disorder, or bipolar disorder. This finding highlights the importance of identification and treatment of comorbid OCS among this vulnerable patient group.

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review

BACKGROUND

Electronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research.

OBJECTIVE

The objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences.

METHODS

We used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results.

RESULTS

We present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized.

CONCLUSIONS

The Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.

Harnessing the power of electronic health records and open natural language data mining to capture meaningful patient experience during routine clinical care

INTRODUCTION

Electronic health records (EHR) are a rich data source for both quality improvement and clinical research. Natural language processing can be harnessed to extract data from these previously difficult to access sources.

OBJECTIVE

The objective of this study was to create and apply a natural language search query to extract EHR data to ask and answer quality improvement questions at a pediatric aerodigestive center.

METHODS

We developed a combined natural language search query to extract clinically meaningful data along with International Statistical Classification of Diseases (ICD10) and Current Procedural Terminology (CPT) code data. This search query was applied to a single pediatric aerodigestive center to answer key clinical questions asked by families. Data were extracted from EHR data from first clinic visit, operative note, microbiology lab report, and pathology report for all new patients from 2020 to 2021. Included as three queries were: 1) if I bring my child to a pediatric aerodigestive center, how often will my child obtain a medical diagnosis without needing an intervention? 2) if my child has a diagnostic procedure, how often will a diagnosis be made? 3) if a diagnosis is made, can it be addressed during that endoscopic intervention?

RESULTS

For the 711 new patients coming to the pediatric aerodigestive center from 2020 to 2021, only 26-32% required an interventional triple endoscopy (rigid/flexible bronchoscopy with esophagoduodenoscopy). Of these triple endoscopies, 75.7% resulted in a positive finding that enabled optimization of that child's care. Of the 221 patients who underwent diagnostic triple endoscopies, 40.7% underwent intervention at the same time for laryngeal cleft (injection or suture, dependent upon age).

CONCLUSION

Here we created an effective model of open language search query to extract meaningful metrics of patient experience from EHR data. This model easily allows the EHR to be harnessed to create retrospective and prospective databases that can be readily queried to answer clinical questions important to patients. Such databases are widely applicable not just to pediatric aerodigestive centers but to any clinical care setting using an EHR.

A Centralized EHR-Based Model for the Recruitment of Rural and Lower Socioeconomic Participants in Pragmatic Trials: A Secondary Analysis of the Diuretic Comparison Project

Importance

Participant diversity is important for reducing study bias and increasing generalizability of comparative effectiveness research.

Objective

Demonstrate the operational efficiency of a centralized electronic health record (EHR)-based model for recruiting difficult-to-reach participants in a pragmatic trial.

Design, Setting, and Participants

This comparative effectiveness study was a secondary analysis of Diuretic Comparison Project, a randomized clinical trial conducted between 2016 and 2022 (mean [SD] follow-up, 2.4 [1.4] years) comparing 2 commonly prescribed antihypertensives, which used an EHR-based recruitment model. Electronic study workflows, in tandem with routine clinical practice, were adapted by 72 Veteran Affairs (VA) primary care networks. Data were analyzed from August to December 2022.

Main Outcomes and Measures

Measures reflecting recruitment capacity (monthly rate), operational efficiency (median time for completion of electronic procedures), and geographic reach (percentage of patients recruited from rural areas) were examined.

Results

A total of 13 523 patients with hypertension (mean [SD] age, 72 [5.4] years; 13 092 male [96.8%]) were recruited from 537 outpatient clinics. Approximately 205 patients were randomized per month and a median of 35 days (Q1-Q3, 23-80 days) was needed to complete electronic recruitment. The annual income was below the national median for 69% of the cohort. Patients from all 50 states, Puerto Rico, and the District of Columbia were included and 45% resided in rural areas.

Conclusions and Relevance

In this secondary analysis of a multicenter pragmatic trial, a centralized EHR-based recruitment model was associated with improved participation from underrepresented groups. These participants often are difficult to reach, with their exclusion potentially biasing trial results; eliminating in-person study visits and local site involvement can minimize barriers for the recruitment of patients from rural and lower socioeconomic areas.

Trial Registration

The Diuretic Comparison Project (DCP) was registered on ClinicalTrials.gov Identifier: NCT02185417.

Procurement of patient medical records from multiple health care facilities for public health research: feasibility, challenges, and lessons learned

Objectives

Studies that combine medical record and primary data are typically conducted in a small number of health care facilities (HCFs) covering a limited catchment area; however, depending on the study objectives, validity may be improved by recruiting a more expansive sample of patients receiving care across multiple HCFs. We evaluate the feasibility of a novel protocol to obtain patient medical records from multiple HCFs using a broad representative sampling frame.

Materials and Methods

In a prospective cohort study on HIV pre-exposure prophylaxis utilization, primary data were collected from a representative sample of community-dwelling participants; voluntary authorization was obtained to access participants' medical records from the HCF at which they were receiving care. Medical record procurement procedures were documented for later analysis.

Results

The cohort consisted of 460 participants receiving care from 122 HCFs; 81 participants were lost to follow-up resulting in 379 requests for medical records submitted to HCFs, and a total of 343 medical records were obtained (91% response rate). Less than 20% of the medical records received were in electronic form. On average, the cost of medical record acquisition was $120 USD per medical record.

Conclusions

Obtaining medical record data on research participants receiving care across multiple HCFs was feasible, but time-consuming and resulted in appreciable missing data. Researchers combining primary data with medical record data should select a sampling and data collection approach that optimizes study validity while weighing the potential benefits (more representative sample; inclusion of HCF-level predictors) and drawbacks (cost, missing data) of obtaining medical records from multiple HCFs.

Infants With Congenital Muscular Torticollis: Demographic Factors, Clinical Characteristics, and Physical Therapy Episode of Care

PURPOSE

To describe demographic factors, baseline characteristics, and physical therapy episodes in infants with congenital muscular torticollis (CMT), examine groups based on physical therapy completion, and identify implications for clinical practice.

METHODS

Retrospective data were extracted from a single-site registry of 445 infants with CMT.

RESULTS

Most infants were male (57%), Caucasian (63%), and firstborn (50%), with torticollis detected by 3 months old (89%) with a left (51%), mild (72%) CMT presentation. Cervical range of motion (ROM) limitations were greatest in passive lateral flexion and active rotation. Sixty-seven percent of infants completed an episode of physical therapy, 25% completed a partial episode, and 8% did not attend visits following the initial examination. Age at examination, ROM, and muscle function differed significantly between groups.

CONCLUSIONS

Physical therapists may use clinical registry data to inform practice for timing of referral, frequency of care, and clinician training to manage infants with CMT.

Payer perceptions of the use of real-world evidence in oncology-based decision making

BACKGROUND: Randomized controlled trials (RCTs), the gold standard of safety and efficacy evidence, are conducted in select patients that may not mirror real-world populations. As a result, healthcare decision makers may have limited information when making formulary decisions, especially in oncology, given accelerated regulatory approvals and niche patient populations. Real-world evidence (RWE) studies may help address these knowledge gaps and help inform oncology formulary decision making. OBJECTIVE: To assess US payer perceptions regarding the use and relevance of RWE in informing oncology formulary decisionmaking. METHODS: A national survey containing single-answer, multiple-answer, and free-response questions evaluated 4 key areas: (1) the value of RWE, (2) barriers to RWE, (3) sources of RWE, and (4) use of RWE in outcomes-based contracting. The survey was distributed to 221 US payers through the Academy of Managed Care Pharmacy (AMCP) Market Insights program in February 2020. Ten additional respondents were invited to discuss the survey results. The survey results were presented primarily as frequencies of responses and were evaluated by the respondent's plan size, type, and geography (regional vs national). Differences in responses for categorical data were compared using a Pearson Chi-Square or a Fisher's Exact test. Two-tailed values are reported and a level of ≤ 0.05 was used to indicate statistical significance. RESULTS: The national survey had a 45.9% response rate, with 106 payers responding. Most were from managed care organizations (MCOs; 47.5%) and pharmacy benefit managers (PBMs; 37.4%), with 54.5% from large plans (≥ 1 million lives) and 45.5% from small plans (< 1 million lives). Respondents were largely pharmacists (89.9%), with 55.6% overall indicating their job was a pharmacy administrator. Most (84.9%) used RWE to inform formulary decisions in oncology to support comparative effectiveness in the absence of head-to-head clinical trials (4.1 on a scale of 1 = Not At All Useful to 5 = Extremely Useful) and validation of National Comprehensive Cancer Network (NCCN) recommendations (4.0). Almost half (41.5%) used RWE results to inform off-label usage decisions. Payers valued RWE pre-launch to inform formulary and contracting decisions and desired real-world comparative effectiveness data post-launch to validate coverage decisions. However, the majority of payers (54.7%) did not conduct their own real-world studies. Commonly considered RWE sources included claims data (79.2%), medical records (68.9%), prospective cohort studies (60.4%), patient registries (36.8%), and patient outcome surveys (33.0%). Barriers to conducting internal RWE studies included the lack of resources and personnel, analytic capabilities, appropriate in-house data, and perceived value in conducting analyses. Payers expressed interest in using outcomes-based contracting in oncology; few have direct experience, and operationalizing through value measurement is challenging. CONCLUSIONS: RWE providing comparative treatment data, validation of NCCN treatment recommendations, and information on off-label usage are appreciated pre launch with post launch validation. DISCLOSURES: Pfizer provided funding for this research, and employees of Pfizer led the development of the survey and contributed to the manuscript as authors. Arondekar and Niyazov are employees of Pfizer; Oderda, Biskupiak, and Brixner are managers of Millcreek Outcomes Group and were paid as consultants on this project. Burgoyne was a consultant for Pfizer on this project. Malone was paid by Millcreek Outcomes as a consultant on this project.

Electronic medical records and primary care quality: Evidence from Manitoba

Improvements in quality of care through supporting decision-making processes and increased efficiency have prompted widespread implementation of electronic medical records (EMRs) in Canada. Using a set of indicators of preventive care, chronic disease management, and hospitalizations due to ambulatory care sensitive conditions (ACSC), this study measures the effect of EMR adoption on quality of primary care measures. Population-based data for the Canadian province of Manitoba are used in a difference-in-differences approach with patient- and time-fixed effects. Evidence of changes in the selected quality-of-care indicators is weak, with preventive care, management of asthma, and hospitalizations showing no significant change due to EMR adoption. A statistically significant increase in the quality of diabetes care was found for EMR users, changes being larger for late EMR adopters which is possibly explained by a network effect. This research demonstrates that measuring whether EMRs prompt changes in the quality of care confronts serious challenges. The rapid evolution and gradual adoption of EMR technology, the inevitable learning/acceptance process by individual health practitioners, and its potential reflection on different patient populations create unmeasurable variables that confound EMRs' impact. This study also underscores the importance of data development to support the economic value of EMRs.

Challenges Frequently Encountered in the Secondary Use of Electronic Medical Record Data for Research

The wide adoption of electronic medical records and subsequent availability of large amounts of clinical data provide a rich resource for researchers. However, the secondary use of clinical data for research purposes is not without limitations. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review to identify current issues related to secondary use of electronic medical record data via MEDLINE and CINAHL databases. All articles published until June 2018 were included. Sixty articles remained after title and abstract review, and four domains of potential limitations were identified: (1) data quality issues, present in 91.7% of the articles reviewed; (2) data preprocessing challenges (53.3%); (3) privacy concerns (18.3%); and (4) potential for limited generalizability (21.7%). Researchers must be aware of the limitations inherent to the use of electronic medical record data for research and consider the potential effects of these limitations throughout the entire study process, from initial conceptualization to the identification of adequate sources that can provide data appropriate for answering the research questions, analysis, and reporting study results. Consideration should also be given to using existing data quality assessment frameworks to facilitate use of standardized data quality definitions and further efforts of standard data quality reporting in publications.

Variation in Documenting Diagnosable Chronic Kidney Disease in General Medical Practice: Implications for Quality Improvement and Research

BACKGROUND

National health surveys indicate that chronic kidney disease (CKD) is an increasingly prevalent condition in Australia, placing a significant burden on the health budget and on the affected individuals themselves. Yet, there are relatively limited data on the prevalence of CKD within Australian general practice patients. In part, this could be due to variation in the terminology used by general practitioners (GPs) to identify and document a diagnosis of CKD. This project sought to investigate the variation in terms used when recording a diagnosis of CKD in general practice.

METHODS

A search of routinely collected de-identified Australian general practice patient data (NPS MedicineWise MedicineInsight from January 1, 2013, to June 1, 2016; collected from 329 general practices) was conducted to determine the terms used. Manual searches were conducted on coded and on "free-text" or narrative information in the medical history, reason for encounter, and reason for prescription data fields.

RESULTS

From this data set, 61 102 patients were potentially diagnosable with CKD on the basis of pathology results, but only 14 172 (23.2%) of these had a term representing CKD in their electronic record. Younger patients with pathology evidence of CKD were more likely to have documented CKD compared with older patients. There were a total of 2090 unique recorded documentation terms used by the GPs for CKD. The most commonly used terms tended to be those included as "pick-list" options within the various general practice software packages' standard "classifications," accounting for 84% of use.

CONCLUSIONS

A diagnosis of CKD was often not documented and, when recorded, it was in a variety of ways. While recording CKD with various terms and in free-text fields may allow GPs to flexibly document disease qualifiers and enter patient specific information, it might inadvertently decrease the quality of data collected from general practice records for clinical audit or research purposes.

Characterization of high healthcare utilizer groups using administrative data from an electronic medical record database

Background

High utilizers (HUs) are a small group of patients who impose a disproportionately high burden on the healthcare system due to their elevated resource use. Identification of persistent HUs is pertinent as interventions have not been effective due to regression to the mean in majority of patients. This study will use cost and utilization metrics to segment a hospital-based patient population into HU groups.

Methods

The index visit for each adult patient to an Academic Medical Centre in Singapore during 2006 to 2012 was identified. Cost, length of stay (LOS) and number of specialist outpatient clinic (SOC) visits within 1 year following the index visit were extracted and aggregated. Patients were HUs if they exceeded the 90th percentile of any metric, and Non-HU otherwise. Seven different HU groups and a Non-HU group were constructed. The groups were described in terms of cost and utilization patterns, socio-demographic information, multi-morbidity scores and medical history. Logistic regression compared the groups’ persistence as a HU in any group into the subsequent year, adjusting for socio-demographic information and diagnosis history.

Results

A total of 388,162 patients above the age of 21 were included in the study. Cost-LOS-SOC HUs had the highest multi-morbidity and persistence into the second year. Common conditions among Cost-LOS and Cost-LOS-SOC HUs were cardiovascular disease, acute cerebrovascular disease and pneumonia, while most LOS and LOS-SOC HUs were diagnosed with at least one mental health condition. Regression analyses revealed that HUs across all groups were more likely to persist compared to Non-HUs, with stronger relationships seen in groups with high SOC utilization. Similar trends remained after further adjustment.

Conclusion

HUs of healthcare services are a diverse group and can be further segmented into different subgroups based on cost and utilization patterns. Segmentation by these metrics revealed differences in socio-demographic characteristics, disease profile and persistence. Most HUs did not persist in their high utilization, and high SOC users should be prioritized for further longitudinal analyses. Segmentation will enable policy makers to better identify the diverse needs of patients, detect gaps in current care and focus their efforts in delivering care relevant and tailored to each segment.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4239-2) contains supplementary material, which is available to authorized users.

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

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.

Mind the gap: observation windows to define periods of event ascertainment as a quality control method for longitudinal electronic health record data

PURPOSE

Use of electronic health records (EHRs) in health research may lead to the false assumption of complete event ascertainment. We estimated "observation windows" (OWs), defined as periods within which the assumption of complete ascertainment of events is more likely to hold, as a quality control approach to reducing the likelihood of this false assumption. We demonstrated the impact of OWs on estimating the rates of type II diabetes mellitus (diabetes) from HIV clinical cohorts.

METHODS

Data contributed by 16 HIV clinical cohorts to the NA-ACCORD were used to identify and evaluate OWs for an operationalized definition of diabetes occurrence as a case study. Procedures included (1) gathering cohort-level data; (2) visualizing and summarizing gaps in observations; (3) systematically establishing start and stop dates during which the assumption of complete ascertainment of diabetes events was reasonable; and (4) visualizing the diabetes OWs relative to the cohort open and close dates to identify immortal person-time. We estimated diabetes occurrence event rates and 95% confidence intervals in the most recent decade that data were available (January 1, 2007, to December 31, 2016).

RESULTS

The number of diabetes events decreased by 17% with the use of the diabetes OWs; immortal person-time was removed decreasing total person-years by 23%. Consequently, the diabetes rate increased from 1.23 (95% confidence interval [1.20, 1.25]) per 100 person-years to 1.32 [1.29, 1.35] per 100 person-years with the use of diabetes OWs.

CONCLUSIONS

As the use of EHR-curated data for event-driven health research continues to expand, OWs have utility as a quality control approach to complete event ascertainment, helping to improve accuracy of estimates by removing immortal person-time when ascertainment is incomplete.

Unstructured Text in EMR Improves Prediction of Death after Surgery in Children

Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children’s Hospital EMR. We used a text mining approach on preoperative notes to obtain a text-based risk score to predict death within 30 days of surgery. In addition, we evaluated the performance of a hybrid model that included the text-based risk score along with structured data pertaining to clinical risk factors. The C-statistic of a logistic regression model with five-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes.

The challenges of assessing effectiveness of lacosamide using electronic medical record databases

PURPOSE

Electronic health record (EHR) databases are a potential source for conducting research to generate real world evidence on patient outcomes. The objective of the study was to evaluate the feasibility of using EHR data to assess seizure outcomes in patients treated with lacosamide (LCM) monotherapy.

METHODS

This was a retrospective cohort study conducted using the Optum clinical EHR database. The study sample comprised patients ≥17 years of age with epilepsy or seizures and treated with LCM monotherapy between 1 January 2009 and 31 December 2013. Structured and unstructured data from prescribed medication and abstracted physician note records were used to identify patients with epilepsy treated with LCM monotherapy and measure seizure frequency outcomes. The index date was the first date of LCM monotherapy, with a 6-month baseline period. Patients were observed for up to 12 months beginning on the index date (follow-up period). The EHR data were not sufficient to compute days supply and explicit duration of LCM and other antiepileptic drug (AED) therapies; therefore, LCM monotherapy was estimated from prescription dates of AEDs. Outcomes were change in seizures per month or change in seizure frequency category from baseline to follow-up. Descriptive statistics were used to describe baseline characteristics and study outcomes.

RESULTS

A total of 10,988 patients with at least one LCM prescription were identified during the study period, 470 of whom met all the selection criteria and were included in the study sample. Although many patients had abstracted physician note records that referred to their seizures, only 3.2% of the patients had seizure frequency information that could be used to quantify the number of seizures per month in both the baseline and follow-up periods; thus, this information could not be used to assess the effectiveness of LCM monotherapy on seizure outcomes.

CONCLUSION

Lacosamide monotherapy effectiveness was not estimated because the EHR prescription record data did not have sufficient information on days supply. Additionally, most patients' records did not contain adequate information to allow for evaluation of quantitative changes in seizure frequency based on the number of seizures per month. More studies are needed to validate these study findings.

Use of an electronic health record data sharing system for identifying current contraceptive use within the WWAMI region Practice and Research Network

OBJECTIVE

To evaluate the ability of electronic health record (EHR) data extracted into a data-sharing system to accurately identify contraceptive use.

STUDY DESIGN

We compared rates of contraceptive use from electronic extraction of EHR data via a data-sharing system and manual abstraction of the EHR among 142 female patients ages 15-49 years from a family medicine clinic within a primary care practice-based research network (PBRN). Cohen's kappa coefficient measured agreement between electronic extraction and manual abstraction.

RESULTS

Manual abstraction identified 62% of women as contraceptive users, whereas electronic extraction identified only 27%. Long acting reversible (LARC) methods had 96% agreement (Cohen's kappa 0.78; confidence interval, 0.57-0.99) between electronic extraction and manual abstraction. EHR data extracted via a data-sharing system was unable to identify barrier or over-the-counter contraceptives.

CONCLUSIONS

Electronic extraction found substantially lower overall rates of contraceptive method use, but produced more comparable LARC method use rates when compared to manual abstraction among women in this study's primary care clinic.

IMPLICATIONS

Quality metrics related to contraceptive use that rely on EHR data in this study's data-sharing system likely under-estimated true contraceptive use.

Castor oil as a natural alternative to labor induction: A retrospective descriptive study

AIM

To describe birthing outcomes among women who consumed castor oil cocktail as part of a freestanding birth center labor induction protocol.

METHODS

De-identified data from birth logs and electronic medical records were entered into SPSS Statistics 22.0 for analysis for all women who received the castor oil cocktail (n=323) to induce labor between January 2008 and May 2015 at a birth center in the United States. Descriptive statistics were analyzed for trends in safety and birthing outcomes.

RESULTS

Of the women who utilized the castor oil cocktail to stimulate labor, 293 (90.7%) birthed vaginally at the birth center or hospital. The incidence of maternal adverse effects (e.g., nausea, vomiting, extreme diarrhea) was less than 7%, and adverse effects of any kind were reported in less than 15% of births. An independent sample t-test revealed that parous women were more likely to birth vaginally at the birth center after using the castor oil cocktail than their nulliparous counterparts (p<.010), while gestational age (p=.26), woman's age (p=.23), and body mass index (p=.28) were not significantly associated.

CONCLUSIONS

Nearly 91% of women in the study who consumed the castor oil cocktail to induce labor were able to give birth vaginally with little to no maternal or fetal complications. Findings indicate further research is needed to compare the safety and effectiveness of natural labor induction methodologies, including castor oil, to commonly used labor induction techniques in a prospective study or clinical trial.

Data Mining Approach to Estimate the Duration of Drug Therapy from Longitudinal Electronic Medical Records

Background:

Electronic Medical Records (EMRs) from primary/ ambulatory care systems present a new and promising source of information for conducting clinical and translational research.

Objectives:

To address the methodological and computational challenges in order to extract reliable medication information from raw data which is often complex, incomplete and erroneous. To assess whether the use of specific chaining fields of medication information may additionally improve the data quality.

Methods:

Guided by a range of challenges associated with missing and internally inconsistent data, we introduce two methods for the robust extraction of patient-level medication data. First method relies on chaining fields to estimate duration of treatment (“chaining”), while second disregards chaining fields and relies on the chronology of records (“continuous”). Centricity EMR database was used to estimate treatment duration with both methods for two widely prescribed drugs among type 2 diabetes patients: insulin and glucagon-like peptide-1 receptor agonists.

Results:

At individual patient level the “chaining” approach could identify the treatment alterations longitudinally and produced more robust estimates of treatment duration for individual drugs, while the “continuous” method was unable to capture that dynamics. At population level, both methods produced similar estimates of average treatment duration, however, notable differences were observed at individual-patient level.

Conclusion:

The proposed algorithms explicitly identify and handle longitudinal erroneous or missing entries and estimate treatment duration with specific drug(s) of interest, which makes them a valuable tool for future EMR based clinical and pharmaco-epidemiological studies. To improve accuracy of real-world based studies, implementing chaining fields of medication information is recommended.

Evaluating a Web-Based Coaching Program Using Electronic Health Records for Patients With Chronic Obstructive Pulmonary Disease in China: Randomized Controlled Trial

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is now the fourth leading cause of death in the world, and it continues to increase in developing countries. The World Health Organization expects COPD to be the third most common cause of death in the world by 2020. Effective and continuous postdischarge care can help patients to maintain good health. The use of electronic health records (EHRs) as an element of community health care is new technology in China.

OBJECTIVE

The aim of this study was to develop and evaluate a Web-based coaching program using EHRs for physical function and health-related quality of life for patients with COPD in China.

METHODS

A randomized controlled trial was conducted from 2008 to 2015 at two hospitals. The control group received routine care and the intervention group received routine care with the addition of the Web-based coaching program using EHRs. These were used to manage patients' demographic and clinical variables, publish relevant information, and have communication between patients and health care providers. Participants were not blinded to group assignment. The effects of the intervention were evaluated by lung function, including percent of forced expiratory volume in 1 second (FEV1%), percent of forced vital capacity (FVC%), peak expiratory flow (PEF), maximum midexpiratory flow; St George's Respiratory Questionnaire (SGRQ); Modified Medical Research Council Dyspnea Scale (MMRC); and 6-Minute Walk Test (6MWT). Data were collected before the program, and at 1, 3, 6, and 12 months after the program.

RESULTS

Of the 130 participants, 120 (92.3%) completed the 12-month follow-up program. There were statistically significant differences in lung function (FEV1%: F1,4=5.47, P=.002; FVC%: F1,4=3.06, P=.02; PEF: F1,4=12.49, P<.001), the total score of SGRQ (F1,4=23.30, P<.001), symptoms of SGRQ (F1,4=12.38, P<.001), the activity of SGRQ (F1,4=8.35, P<.001), the impact of SGRQ (F1,4=12.26, P<.001), MMRC (F1,4=47.94, P<.001), and 6MWT (F1,4=35.54, P<.001) between the two groups with the variation of time tendency.

CONCLUSIONS

The Web-based coaching program using EHRs in China appears to be useful for patients with COPD when they are discharged from hospital into the community. It promotes the sharing of patients' medical information by hospital and community nurses, and achieves dynamic management and follow-up analysis for patients' disease. In addition, this program can postpone the decreasing rate of lung function, improve quality of life, decrease dyspnea, and increase physical capacity.

Knowledge Translation in Rehabilitation: A Shared Vision

PURPOSE

Advances in rehabilitation provide the infrastructure for research and clinical data to improve care and patient outcomes. However, gaps between research and practice are prevalent. Knowledge translation (KT) aims to decrease the gap between research and its clinical use. This special communication summarizes KT-related proceedings from the 2016 IV STEP conference, describes current KT in rehabilitation science, and provides suggestions for its application in clinical care.

SUMMARY OF KEY POINTS

We propose a vision for rehabilitation clinical practice and research that includes the development, adaptation, and implementation of evidence-based practice recommendations, which will contribute to a learning health care system. A clinical research culture that supports this vision and methods to engage key stakeholders to innovate rehabilitation science and practice are described.

CONCLUSIONS

Through implementation of this vision, we can lead an evolution in rehabilitation practice to ultimately prevent disabilities, predict better outcomes, exploit plasticity, and promote participation.

Survival analysis using primary care electronic health record data: A systematic review of the literature

PURPOSE

An emerging body of research involves observational studies in which survival analysis is applied to data obtained from primary care electronic health records (EHRs). This systematic review of these studies examined the utility of using this approach.

METHOD

An electronic literature search of the Scopus, PubMed, Web of Science, CINAHL, and Cochrane databases was conducted. Search terms and exclusion criteria were chosen to select studies where survival analysis was applied to the data extracted wholly from EHRs used in primary care medical practice.

RESULTS

A total of 46 studies that met the inclusion criteria for the systematic review were examined. All were published within the past decade (2005-2014) with a majority ( n = 26, 57%) being published between 2012 and 2014. Even though citation rates varied from nil to 628, over half ( n = 27, 59%) of the studies were cited 10 times or more. The median number of subjects was 18,042 with five studies including over 1,000,000 patients. Of the included studies, 35 (76%) were published in specialty journals and 11 (24%) in general medical journals. The many conditions studied largely corresponded well with conditions important to general practice.

CONCLUSION

Survival analysis applied to primary care electronic medical data is a research approach that has been frequently used in recent times. The utility of this approach was demonstrated by the ability to produce research with large numbers of subjects, across a wide range of conditions and with the potential of a high impact. Importantly, primary care data were thus available to inform primary care practice.

Which Propensity Score Method Best Reduces Confounder Imbalance? An Example From a Retrospective Evaluation of a Childhood Obesity Intervention

BACKGROUND

Propensity score (PS) methods are increasingly being employed by researchers to reduce bias arising from confounder imbalance when using observational data to examine intervention effects.

OBJECTIVE

The purpose of this study was to examine PS theory and methodology and compare application of three PS methods (matching, stratification, weighting) to determine which best improves confounder balance.

METHODS

Baseline characteristics of a sample of 20,518 school-aged children with severe obesity (of whom 1,054 received an obesity intervention) were assessed prior to PS application. Three PS methods were then applied to the data to determine which showed the greatest improvement in confounder balance between the intervention and control group. The effect of each PS method on the outcome variable-body mass index percentile change at one year-was also examined. SAS 9.4 and Comprehensive Meta-analysis statistical software were used for analyses.

RESULTS

Prior to PS adjustment, the intervention and control groups differed significantly on seven of 11 potential confounders. PS matching removed all differences. PS stratification and weighting both removed one difference but created two new differences. Sensitivity analyses did not change these results. Body mass index percentile at 1 year decreased in both groups. The size of the decrease was smaller in the intervention group, and the estimate of the decrease varied by PS method.

DISCUSSION

Selection of a PS method should be guided by insight from statistical theory and simulation experiments, in addition to observed improvement in confounder balance. For this data set, PS matching worked best to correct confounder imbalance. Because each method varied in correcting confounder imbalance, we recommend that multiple PS methods be compared for ability to improve confounder balance before implementation in evaluating treatment effects in observational data.

Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project

OBJECTIVES

We sought to use natural language processing to develop a suite of language models to capture key symptoms of severe mental illness (SMI) from clinical text, to facilitate the secondary use of mental healthcare data in research.

DESIGN

Development and validation of information extraction applications for ascertaining symptoms of SMI in routine mental health records using the Clinical Record Interactive Search (CRIS) data resource; description of their distribution in a corpus of discharge summaries.

SETTING

Electronic records from a large mental healthcare provider serving a geographic catchment of 1.2 million residents in four boroughs of south London, UK.

PARTICIPANTS

The distribution of derived symptoms was described in 23 128 discharge summaries from 7962 patients who had received an SMI diagnosis, and 13 496 discharge summaries from 7575 patients who had received a non-SMI diagnosis.

OUTCOME MEASURES

Fifty SMI symptoms were identified by a team of psychiatrists for extraction based on salience and linguistic consistency in records, broadly categorised under positive, negative, disorganisation, manic and catatonic subgroups. Text models for each symptom were generated using the TextHunter tool and the CRIS database.

RESULTS

We extracted data for 46 symptoms with a median F1 score of 0.88. Four symptom models performed poorly and were excluded. From the corpus of discharge summaries, it was possible to extract symptomatology in 87% of patients with SMI and 60% of patients with non-SMI diagnosis.

CONCLUSIONS

This work demonstrates the possibility of automatically extracting a broad range of SMI symptoms from English text discharge summaries for patients with an SMI diagnosis. Descriptive data also indicated that most symptoms cut across diagnoses, rather than being restricted to particular groups.

A valuable approach to the use of electronic medical data in primary care research: Panning for gold

Background:

Electronic medical data (EMD) from electronic health records of general practice computer systems have enormous research potential, yet many variables are unreliable.

Objective:

The aim of this study was to compare selected data variables from general practice EMD with a reliable, representative national dataset (Bettering the Evaluation and Care of Health (BEACH)) in order to validate their use for primary care research.

Method:

EMD variables were compared with encounter data from the nationally representative BEACH program using χ2 tests and robust 95% confidence intervals to test their validity (measure what they reportedly measure). The variables focused on for this study were patient age, sex, smoking status and medications prescribed at the visit.

Results:

The EMD sample from six general practices in the Illawarra region of New South Wales, Australia, yielded data on 196,515 patient encounters. Details of 90,553 encounters were recorded in the 2013 BEACH dataset from 924 general practitioners. No significant differences in patient age ( p = 0.36) or sex ( p = 0.39) were found. EMD had a lower rate of current smokers and higher average scripts per visit, but similar prescribing distribution patterns.

Conclusion:

Validating EMD variables offers avenues for improving primary care delivery and measuring outcomes of care to inform clinical practice and health policy.

Balancing the Evidence: How to Reconcile the Results of Observational Studies vs. Randomized Clinical Trials in Dialysis

Because large randomized clinical trials (RCTs) in dialysis have been relatively scarce, evidence-based dialysis care has depended heavily on the results of observational studies. However, when results from RCTs appear to contradict the findings of observational studies, nephrologists are left to wonder which type of study they should believe. In this editorial, we explore the key differences between observational studies and RCTs in the context of such seemingly conflicting studies in dialysis. Confounding is the major limitation of observational studies, whereas low statistical power and problems with external validity are more likely to limit the findings of RCTs. Differences in the specification of the population, exposure, and outcomes can also contribute to different results among RCTs and observational studies. Rigorous methods are required regardless of what type of study is conducted, and readers should not automatically assume that one type of study design is superior to the other. Ultimately, dialysis care requires both well-designed, well-conducted observational studies and RCTs to move the field forward.

Antidepressant medication use and glycaemic control in co-morbid type 2 diabetes and depression

OBJECTIVE

Depression is prevalent in diabetes and is associated with increased risks of hyperglycaemia, morbidity and mortality. The effect of antidepressant medication (ADM) on glycaemic control is uncertain owing to a paucity of relevant data. We sought to determine whether the use of ADM is associated with glycaemic control in depressed patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

A retrospective cohort study (n = 1399) was conducted using electronic medical record registry data of ambulatory primary care visits from 2008 to 2013. Depression and type 2 diabetes were identified from ICD-9-CM codes; ADM use was determined from prescription orders; and glycaemic control was determined from measures of glycated haemoglobin (A1c). Good glycaemic control was defined as A1c < 7.0% (53 mmol/mol). Generalized estimating equations were used to determine the effect of depression and ADM use on glycaemic control.

RESULTS

Good glycaemic control was achieved by 50.9% of depressed subjects receiving ADM versus 34.6% of depressed subjects without ADM. After adjusting for covariates, depressed patients receiving ADM were twice as likely as those not receiving ADM to achieve good glycaemic control (odds ratio = 1.95; 95% confidence interval: 1.02-3.71).

CONCLUSIONS

In this retrospective cohort study of a large sample of primary care patients with type 2 diabetes, ADM use was associated with improved glycaemic control.

Benefits of applying a proxy eligibility period when using electronic health records for outcomes research: a simulation study

BACKGROUND

Electronic health records (EHRs) can provide valuable data for outcomes research. However, unlike administrative claims databases, EHRs lack eligibility tables or a standard way to define the benefit coverage period, which could lead to underreporting of healthcare utilization or outcomes, and could result in surveillance bias. We tested the effect of using a proxy eligibility period (eligibility proxy) when estimating a range of health resource utilization and outcomes parameters under varying degrees of missing encounter data.

METHODS

We applied an eligibility proxy to create a benchmark cohort of chronic obstructive pulmonary disease (COPD) patients with 12 months of follow-up, with the assumption of no missing encounter data. The benchmark cohort provided parameter estimates for comparison with 9,000 simulated datasets representing 10-90% of COPD patients (by 10th percentiles) with between 1 and 11 months of continuous missing data. Two analyses, one for datasets using an eligibility proxy and one for those without an eligibility proxy, were performed on the 9,000 datasets to assess estimator performance under increasing levels of missing data. Estimates for each study variable were compared with those from the benchmark dataset, and performance was evaluated using bias, percentage change, and root-mean-square error.

RESULTS

The benchmark dataset contained 6,717 COPD patients, whereas the simulated datasets where the eligibility proxy was applied had between 671 and 6,045 patients depending on the percentage of missing data. Parameter estimates had better performance when an eligibility proxy based on the first and last month of observed activity was applied. This finding was consistent across a range of variables representing patient comorbidities, symptoms, outcomes, health resource utilization, and medications, regardless of the measures of performance used. Without the eligibility proxy, all evaluated parameters were consistently underestimated.

CONCLUSION

In a large COPD patient population, this study demonstrated that applying an eligibility proxy to EHR data based on the earliest and latest months of recorded activity minimized the impact of missing data in outcomes research and improved the accuracy of parameter estimates by reducing surveillance bias. This approach may address the problem of missing data in a wide range of EHR outcomes studies.

Superficial venous thrombosis: deeper than meets the eye?

In this issue of Blood, Cannegieter et al use the Danish National Patient Registry to report on the high incidence of venous thromboembolism (VTE), mortality, and arterial thrombosis following a diagnosis of superficial vein thrombosis (SVT).