Electronic Health Record and Problem Lists in Leeds, United Kingdom: Variability of general practitioners' views

Beyond empathy training for practitioners: Cultivating empathic healthcare systems and leadership

Empathic care benefits patients and practitioners, and empathy training for practitioners can enhance empathy. However, practitioners do not operate in a vacuum. For empathy to thrive, healthcare consultations must be situated in a nurturing milieu, guided by empathic, compassionate leaders. Empathy will be suppressed, or even reversed if practitioners are burned out and working in an unpleasant, under-resourced environment with increasingly poorly served and dissatisfied patients. Efforts to enhance empathy must therefore go beyond training practitioners to address system-level factors that foster empathy. These include patient education, cultivating empathic leadership, customer service training for reception staff, valuing cleaning and all ancillary staff, creating healing spaces, and using appropriate, efficiency saving technology to reduce the administrative burden on healthcare practitioners. We divide these elements into environmental factors, organisational factors, job factors, and individual characteristics.

Implementing Electronic Health Records in Primary Care Using the Theory of Change: A Nigerian Case Study (Preprint)

Background

Objective

Methods

Results

Conclusions

Factors affecting the manual linking of clinical progress notes to problems in daily clinical practice: A retrospective quantitative analysis and cross sectional survey

This cross sectional study examines how patient characteristics, doctor characteristics, and doctors' education and attitudes affect the extent to which doctors link progress notes to clinical problems. The independent effects of patient characteristics on the linking of notes was examined with a mixed model logistic regression. The effects of doctor characteristics and doctors' education and attitudes on the link ratio was analyzed with univariate analysis of variance. A survey was used to obtain arguments and attitudes on linking notes. For "patient characteristics", the odds of linking increased with an increase in the number of problems or hospital days, decreased, with an increase in the number of involved doctors, medical specialties or the number of notes. For "doctor characteristics", the link ratio increased with more work experience. For "doctors' education and attitudes", the link ratio increased with more familiarity in linking notes and belief in the added value of problem oriented charting. "Overview" was the most cited reason for linking; "I don't know how" the most cited reason for not linking. There is a huge variation within and between all disciplines. Important arguments, for and against, are found. Recommendations for policymakers and medical leadership are given to maximize the benefits.

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

Background

Accurate, coded problem lists are valuable for data reuse, including clinical decision support and research. However, healthcare providers frequently modify coded diagnoses by including or removing common contextual properties in free-text diagnosis descriptions: uncertainty (suspected glaucoma), laterality (left glaucoma) and temporality (glaucoma 2002). These contextual properties could cause a difference in meaning between underlying diagnosis codes and modified descriptions, inhibiting data reuse. We therefore aimed to develop and evaluate an algorithm to identify these contextual properties.

Methods

A rule-based algorithm called UnLaTem (Uncertainty, Laterality, Temporality) was developed using a single-center dataset, including 288,935 diagnosis descriptions, of which 73,280 (25.4%) were modified by healthcare providers. Internal validation of the algorithm was conducted with an independent sample of 980 unique records. A second validation of the algorithm was conducted with 996 records from a Dutch multicenter dataset including 175,210 modified descriptions of five hospitals. Two researchers independently annotated the two validation samples. Performance of the algorithm was determined using means of the recall and precision of the validation samples. The algorithm was applied to the multicenter dataset to determine the actual prevalence of the contextual properties within the modified descriptions per specialty.

Results

For the single-center dataset recall (and precision) for removal of uncertainty, uncertainty, laterality and temporality respectively were 100 (60.0), 99.1 (89.9), 100 (97.3) and 97.6 (97.6). For the multicenter dataset for removal of uncertainty, uncertainty, laterality and temporality it was 57.1 (88.9), 86.3 (88.9), 99.7 (93.5) and 96.8 (90.1). Within the modified descriptions of the multicenter dataset, 1.3% contained removal of uncertainty, 9.9% uncertainty, 31.4% laterality and 9.8% temporality.

Conclusions

We successfully developed a rule-based algorithm named UnLaTem to identify contextual properties in Dutch modified diagnosis descriptions. UnLaTem could be extended with more trigger terms, new rules and the recognition of term order to increase the performance even further. The algorithm’s rules are available as additional file 2. Implementing UnLaTem in Dutch hospital systems can improve precision of information retrieval and extraction from diagnosis descriptions, which can be used for data reuse purposes such as decision support and research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01477-y.

Consultation analysis: use of free text versus coded text

General practice in the United Kingdom has been using electronic health records for over two decades, but coding clinical information remains poor. Lack of interest and training are considerable barriers preventing code use levels improvement. Tailored training could be the way forward, to break barriers in the uptake of coding; to do so it is paramount to understand coding use of the particular clinicians, to recognise their needs. It should be possible to easily assess text quantity and quality in medical consultations. A tool to measure these parameters, which could be used to tailor training needs and assess change, is demonstrated. The tool is presented and a preliminary study using a randomised sample of five recent consultations from thirteen different clinicians is used as an example. The tool, based on using a word processor and a spread-sheet, allowed quantitative analysis among clinicians while word clouds permitted a qualitative comparison between coded and free text. The average amount of free text per consultation was 68.2 words, (ranging from 25.4 and 130.2 among clinicians); an average of 6% of the text was coded (ranging from 0 to 13%). Patterns among clinicians could be identified. Using Word cloud, a different text use was demonstrated depending on its purpose. Some free text could be turned into code but nomenclature probably prevented some of the codings, like the expression of time. This proof of concept demonstrated that it is possible to calculate what percentage of consultations are coded and what codes are used. This allowed understanding clinicians’ preferences; training needs and gaps in nomenclature.

Factors Influencing Problem List Use in Electronic Health Records-Application of the Unified Theory of Acceptance and Use of Technology

BACKGROUND

Problem-oriented electronic health record (EHR) systems can help physicians to track a patient's status and progress, and organize clinical documentation, which could help improving quality of clinical data and enable data reuse. The problem list is central in a problem-oriented medical record. However, current problem lists remain incomplete because of the lack of end-user training and inaccurate content of underlying terminologies. This leads to modifications of diagnosis code descriptions and use of free-text notes, limiting reuse of data.

OBJECTIVES

We aimed to investigate factors that influence acceptance and actual use of the problem list, and used these to propose recommendations, to increase the value of problem lists for (re)use.

METHODS

Semistructured interviews were conducted with physicians, heads of medical departments, and data quality experts, who were invited through snowball sampling. The interviews were transcribed and coded. Comments were fitted in constructs of the validated framework unified theory of acceptance user technology (UTAUT), and were discussed in terms of facilitators and barriers.

RESULTS

In total, 24 interviews were conducted. We found large variability in attitudes toward problem list use. Barriers included uncertainty about the responsibility for maintaining the problem list and little perceived benefits. Facilitators included the (re)design of policies, improved (peer-to-peer) training to increase motivation, and positive peer feedback and monitoring. Motivation is best increased through sharing benefits relevant in the care process, such as providing overview, timely generation of discharge or referral letters, and reuse of data. Furthermore, content of the underlying terminology should be improved and the problem list should be better presented in the EHR system.

CONCLUSION

To let physicians accept and use the problem list, policies and guidelines should be redesigned, and prioritized by supervising staff. Additionally, peer-to-peer training on the benefits of using the problem list is needed.