Contextual property detection in Dutch diagnosis descriptions for uncertainty, laterality and temporality

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

BACKGROUND

In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible.

OBJECTIVE

The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks.

METHODS

This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English.

RESULTS

We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%).

CONCLUSIONS

CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

Effective and feasible interventions to improve structured EHR data registration and exchange: A concept mapping approach and exploration of practical examples in the Netherlands

BACKGROUND

Data in Electronic Health Records (EHRs) is often poorly structured and standardized, which hampers data reuse. Research described some examples of interventions to increase and improve structured and standardized data, such as guidelines and policies, training and user friendly EHR interfaces. However, little is known about the translation of this knowledge into practical solutions. Our study aimed to specify the most effective and feasible interventions that enable better structured and standardized EHR data registration and described practical examples of successfully implemented interventions.

METHODS

A concept mapping approach was used to determine feasible interventions that were considered to be effective or have been successfully implemented in Dutch hospitals. A focus group was held with Chief Medical Information Officers and Chief Nursing Information Officers. After interventions were determined, multidimensional scaling and cluster analysis were performed to categorize sorted interventions using Groupwisdom™, an online tool for concept mapping. Results are presented as Go-Zone plots and cluster maps. Following, semi-structured interviews were conducted to describe practical examples of successful interventions.

RESULTS

Interventions were classified into seven clusters ranked from highest to lowest perceived effectiveness: (1) education on usefulness and need; (2) strategic and (3) tactical organizational policies; (4) national policy; (5) monitoring and adjusting data (6) structure of and support from the EHR and (7) support in the registration process (EHR independent). Interviewees emphasized the following interventions proven successful in their practice: an enthusiastic ambassador per specialty who is responsible for educating peers by increasing awareness of the direct benefit of structured and standardized data registration; dashboards for continuous feedback on data quality; and EHR functionalities that support (automating) the registration process.

CONCLUSIONS

Our study provided a list of effective and feasible interventions including practical examples of interventions that have been successful. Organizations should continue to share their best practices to learn from and attempted interventions to prevent implementation of ineffective interventions.

Use of an open-source electronic health record to establish a "virtual hospital": A tale of two curricula

BACKGROUND

The electronic health record (EHR) is central to medical informatics. Its use is also recognized as an important skill for future clinicians. Typically, medical students' first exposure to an EHR is when they start their clinical internships, and medical informatics students may or may not get experience with an EHR before graduation. We describe the process of implementing an open-source EHR in two curricula: Medicine and Medical informatics. For medical students, the primary goals were to allow students to practice analyzing information from the EHR, creating therapeutic plans, and communicating with their colleagues via the EHR before they start their first clinical rotations. For medical informatics students, the primary goal was to give students hands-on experience with creating decision support in an EHR.

APPROACH

We used the OpenMRS electronic health record with a custom decision support module based on Arden Syntax. Medical students needed a secure, stable environment to practice medical reasoning. Medical informatics students needed a more isolated system to experiment with the EHR's internal configuration. Both student groups needed synthetic patient cases that were realistic, but in different aspects. For medical students, it is essential that these cases are clinically consistent, and events unfold in a logical order. By contrast, synthetic data for medical informatics students should mimic the data quality problems found in real patient data.

OUTCOMES

Medical informatics students show more mature reasoning about data quality issues and workflow integration than prior to using the EHR. Comments on both course evaluations have been positive, including comments on how working with a real-world EHR provides a realistic experience.

CONCLUSION

The open-source EHR OpenMRS has proven to be a valuable addition to both the medicine and medical informatics curriculum. Both sets of students experience use of the EHR as giving them valuable, realistic learning experiences.

Inaccurate recording of routinely collected data items influences identification of COVID-19 patients

Background

During the Coronavirus disease 2019 (COVID-19) pandemic it became apparent that it is difficult to extract standardized Electronic Health Record (EHR) data for secondary purposes like public health decision-making. Accurate recording of, for example, standardized diagnosis codes and test results is required to identify all COVID-19 patients. This study aimed to investigate if specific combinations of routinely collected data items for COVID-19 can be used to identify an accurate set of intensive care unit (ICU)-admitted COVID-19 patients.

Methods

The following routinely collected EHR data items to identify COVID-19 patients were evaluated: positive reverse transcription polymerase chain reaction (RT-PCR) test results; problem list codes for COVID-19 registered by healthcare professionals and COVID-19 infection labels. COVID-19 codes registered by clinical coders retrospectively after discharge were also evaluated. A gold standard dataset was created by evaluating two datasets of suspected and confirmed COVID-19-patients admitted to the ICU at a Dutch university hospital between February 2020 and December 2020, of which one set was manually maintained by intensivists and one set was extracted from the EHR by a research data management department. Patients were labeled ‘COVID-19′ if their EHR record showed diagnosing COVID-19 during or right before an ICU-admission. Patients were labeled ‘non-COVID-19′ if the record indicated no COVID-19, exclusion or only suspicion during or right before an ICU-admission or if COVID-19 was diagnosed and cured during non-ICU episodes of the hospitalization in which an ICU-admission took place. Performance was determined for 37 queries including real-time and retrospective data items. We used the F₁ score, which is the harmonic mean between precision and recall. The gold standard dataset was split into one subset including admissions between February and April and one subset including admissions between May and December to determine accuracy differences.

Results

The total dataset consisted of 402 patients: 196 ‘COVID-19′ and 206 ‘non-COVID-19′ patients. F₁ scores of search queries including EHR data items that can be extracted real-time ranged between 0.68 and 0.97 and for search queries including the data item that was retrospectively registered by clinical coders F₁ scores ranged between 0.73 and 0.99. F₁ scores showed no clear pattern in variability between the two time periods.

Conclusions

Our study showed that one cannot rely on individual routinely collected data items such as coded COVID-19 on problem lists to identify all COVID-19 patients. If information is not required real-time, medical coding from clinical coders is most reliable. Researchers should be transparent about their methods used to extract data. To maximize the ability to completely identify all COVID-19 cases alerts for inconsistent data and policies for standardized data capture could enable reliable data reuse.