The value of structured data elements from electronic health records for identifying subjects for primary care clinical trials

Abstract Background: An increasing number of clinical trials are conducted in primary care settings. Making better use of existing data in the electronic health records to identify eligible subjects can improve efficiency of such studies. Our study aims to quantify the proportion of eligibility criteria that can be addressed with data in electronic health records and to compare the content of eligibility criteria in primary care with previous work. Methods: Eligibility criteria were extracted from primary care studies downloaded from the UK Clinical Research Network Study Portfolio. Criteria were broken into elemental statements. Two expert independent raters classified each statement based on whether or not structured data items in the electronic health record can be used to determine if the statement was true for a specific patient. Disagreements in classification were discussed until 100 % agreement was reached. Statements were also classified based on content and the percentages of each category were compared to two similar studies reported in the literature. Results: Eligibility criteria were retrieved from 228 studies and decomposed into 2619 criteria elemental statements. 74 % of the criteria elemental statements were considered likely associated with structured data in an electronic health record. 79 % of the studies had at least 60 % of their criteria statements addressable with structured data likely to be present in an electronic health record. Based on clinical content, most frequent categories were: "disease, symptom, and sign", "therapy or surgery", and "medication" (36 %, 13 %, and 10 % of total criteria statements respectively). We also identified new criteria categories related to provider and caregiver attributes (2.6 % and 1 % of total criteria statements respectively). Conclusions: Electronic health records readily contain much of the data needed to assess patients' eligibility for clinical trials enrollment. Eligibility criteria content categories identified by our study can be incorporated as data elements in electronic health records to facilitate their integration with clinical trial management systems

Patient recruitment, feasibility evaluations and use of electronic health records in clinical trials - A Nordic approach

Clinical trials constitute an important cornerstone for the development of new drugs. Patient recruitment is one of the main challenges in clinical trials. Pharmaceutical companies apply feasibility evaluations to identify potential countries, investigators and study sites for their trials and to evaluate their potential for successful patient recruitment. Electronic health records (EHR) maintained by health care providers are regarded as one potential tool for improving patient identification and recruitment for clinical trials. This study investigated patient recruitment and trial feasibility evaluations in the Nordic countries and the role and usability of EHR data in those processes. The pharmaceutical industry’s view was investigated by conducting semi-structured qualitative interviews of 21 respondents from Finland, Sweden, Norway and Denmark. Additionally, the usability of one commercial EHR research platform for identifying patients from Turku University Hospital’s EHR system was tested in comparison with a manual search. The success or failure of patient recruitment was influenced by many sponsorrelated, investigator/site-related, patient-related, collaboration-related and start-uprelated factors. Most trials had recruited their patients by reviewing the hospitals’ EHR data, but its use was much less frequent already during the feasibility evaluation phase. Feasibility evaluation was found to be a complex and time-consuming process for estimating the number of potential trial patients. The sponsors did not use HER tools for such evaluations, mainly because of legislative barriers. Although the HER data search tools have limitations in accuracy, they were seen to have great potential for identifying trial participants from the hospital EHR, for example by reducing the manual work. The comprehensive data in the EHR systems in the Nordic countries offer a possibility for more accurate identification of trial participants in the feasibility evaluations and may thus contribute to the success of recruitment. The data protection legislation and its interpretation should be harmonized for the use of EHR data. Continuous improvements in the EHR systems’ technical accuracy and data quality will be needed to enhance the successful use of EHR data in future clinical trials.Potilasrekrytointi, toteutettavuuden arviointi ja elektronisten potilastietojärjestelmien hyödyntäminen kliinisissä tutkimuksissa – Pohjoismainen näkökulma Kliiniset lääketutkimukset ovat uusien lääkkeiden kehityksen kulmakivi. Tutkimuspotilaiden rekrytointi on merkittävä haaste näissä tutkimuksissa. Lääkeyritykset tekevät toteutettavuusarviointeja tunnistaakseen potentiaalisia tutkimukseen osallistuvia maita, tutkijoita ja tutkimuskeskuksia ja arvioidakseen niiden mahdollisuuksia onnistua potilaiden rekrytoinnissa. Terveydenhuolto-organisaatioiden ylläpitämät elektroniset potilastietojärjestelmät (EHR) ovat tässä eräs mahdollinen työkalu. Tässä tutkimuksessa tutkittiin potilaiden rekrytointia ja tutkimusten toteutettavuusarviointeja Pohjoismaissa ja EHR:n roolia ja käytettävyyttä näissä prosesseissa. Näitä tekijöitä tekijöitä tutkittiin lääketeollisuuden näkökulmasta laadullisilla teemahaastatteluilla (21 haastateltavaa Suomesta, Ruotsista, Norjasta ja Tanskasta). Yhden kaupallisesti saatavilla olevan EHR-hakutyökalun tarkkuutta halutun potilasjoukon löytämisessä verrattiin perinteiseen, manuaaliseen hakuun Turun yliopistollisen sairaalan potilastietojärjestelmästä. Potilaiden rekrytoinnin onnistumiseen tai epäonnistumiseen vaikutti moni toimeksiantajaan, tutkijaan/tutkimuskeskukseen, potilaaseen ja tutkimuksen aloitustoimenpiteisiin liittyvä tekijä sekä näiden tahojen yhteistyö. Valtaosassa tutkimuksista tutkittavat rekrytoitiin keskuksen omista potilaista EHR:a hyödyntäen, mutta EHR:n käyttö potilasmäärän arvioinnissa ennen tutkimuksen alkua oli vähäistä. Toteutettavuusarvioinneissa tehdyt potilasmäärien arviot nähtiin monimutkaisina ja aikaa vievinä prosesseina. Toimeksiantajat eivät käyttäneet EHRtyökaluja lainkaan, pääasiassa tietosuojalainsäädäntöön liittyvistä syistä. Vaikka EHR-hakutyökalujen tarkkuudella on rajoitteensa, niitä voidaan hyödyntää esimerkiksi vähentämään manuaalista työtä potilaiden identifioinnissa. Terveydenhuollon kattavat EHR-järjestelmät tarjoavat Pohjoismaissa hyvän mahdollisuuden tutkimuspotilaiden tarkempaan identifiointiin, joka omalta osaltaan vaikuttaa rekrytoinnin onnistumismahdollisuuksiin. Tietosuojalainsäädäntöä ja sen tulkintoja on harmonisoitava EHR:n käytön hyödyntämiseksi. EHR-hakujen teknistä tarkkuutta ja tiedon laatua on edelleen parannettava sen menestyksekkään käytön lisäämiseksi tulevaisuuden kliinisissä tutkimuksissa

Feedback GAP : study protocol for a cluster-randomized trial of goal setting and action plans to increase the effectiveness of audit and feedback interventions in primary care

Peer reviewedPublisher PD

Understanding Disease Heterogeneity and Patient Characteristics in Patients with Amyotrophic Lateral Sclerosis (ALS)

Background: Amytrophic lateral sclerosis (ALS) is a fatal neurologic disease that is projected to double in worldwide incidence in the next 20 years. The heterogenic nature of the disease and relatively limited research data, compared to non-rare diseases, have made it difficult for clinician researchers to alter the course of the disease within the short life expectancy after symptom onset. Method: This was a mixed-method retrospective review and live sampling study using three distinct data sources. Retrospective data was abstracted from the electronic medical record systems for a select group of ALS patients seen at the University of California, Irvine Neuromuscular Center (UCI NMC). Additional retrospective datasets curated by the Pooled Resources Open-Access Clinical Trials (PRO-ACT) database were also analyzed. Observational data was collected using a 9-item survey developed on Google Forms and disseminated through the ALS Association Golden West Chapter. The items measured symptom onset, diagnostic journey, and patient demographics.Results: The analyses confirmed current reports of higher disease incidence in Caucasian populations, usually comprising at least 60% of each dataset. The gender prevalence towards males was only observed in the PRO-ACT dataset. There was also a difference in mean age between PRO-ACT (56 years), UCI (61 years), and Online Questionnaire respondents (66 years). Discussion: Ultimately retrospective data analyses were limited by substantial missing, not at random data. Large data repositories can bridge the gap between non-rare and rare disease research, but only with robust and methodologic data collection across all participating sites

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication