Implementing Structured Clinical Templates at a Single Tertiary Hospital: Survey Study

Clinicians' Perspectives on the Telehealth Serious Illness Care Program for Older Adults With Myeloid Malignancies: Single-Arm Pilot Study

BACKGROUND

Serious illness conversations may help patients avoid unwanted treatments. We previously piloted the telehealth Serious Illness Care Program (SICP) for older adults with acute myeloid leukemia and myelodysplastic syndrome.

OBJECTIVE

In this study, we aimed to understand the experience of the telehealth SICP from the clinician's perspective.

METHODS

We studied 10 clinicians who delivered the telehealth SICP to 20 older adults with acute myeloid leukemia or myelodysplastic syndrome. Quantitative outcomes included confidence and acceptability. Confidence was measured using a 22-item survey (range 1-7; a higher score is better). Acceptability was measured using an 11-item survey (5-point Likert scale). Hypothesis testing was performed at α=.10 (2-tailed) due to the pilot nature and small sample size. Clinicians participated in audio-recorded qualitative interviews at the end of the study to discuss their experience.

RESULTS

A total of 8 clinicians completed the confidence measure and 7 clinicians completed the acceptability measure. We found a statistically significant increase in overall confidence (mean increase of 0.5, SD 0.6; P=.03). The largest increase in confidence was in helping families with reconciliation and goodbye (mean 1.4, SD 1.5; P=.04). The majority of clinicians agreed that the format was simple (6/7, 86%) and easy to use (6/7, 86%). Clinicians felt that the telehealth SICP was effective in understanding their patients' values about end-of-life care (7/7, 100%). A total of three qualitative themes emerged: (1) the telehealth SICP deepened relationships and renewed trust; (2) each telehealth SICP visit felt unique and personal in a positive way; and (3) uninterrupted, unrushed time optimized the visit experience.

CONCLUSIONS

The telehealth SICP increased confidence in having serious illness conversations while deepening patient-clinician relationships.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04745676; https://www.clinicaltrials.gov/study/NCT04745676.

Improving quality of care by standardising patient data collection in electronic medical records in an oncology department in Spain

BACKGROUND

Evaluation of quality of care in oncology is key in ensuring patients receive adequate treatment. American Society of Clinical Oncology's (ASCO) Quality Oncology Practice Initiative (QOPI) Certification Program (QCP) is an international initiative that evaluates quality of care in outpatient oncology practices.

METHODS

We retrospectively reviewed free-text electronic medical records from patients with breast cancer (BR), colorectal cancer (CRC) or non-small cell lung cancer (NSCLC). In a baseline measurement, high scores were obtained for the nine disease-specific measures of QCP Track (2021 version had 26 measures); thus, they were not further analysed. We evaluated two sets of measures: the remaining 17 QCP Track measures, as well as these plus other 17 measures selected by us (combined measures). Review of data from 58 patients (26 BR; 18 CRC; 14 NSCLC) seen in June 2021 revealed low overall quality scores (OQS)-below ASCO's 75% threshold-for QCP Track measures (46%) and combined measures (58%). We developed a plan to improve OQS and monitored the impact of the intervention by abstracting data at subsequent time points.

RESULTS

We evaluated potential causes for the low OQS and developed a plan to improve it over time by educating oncologists at our hospital on the importance of improving collection of measures and highlighting the goal of applying for QOPI certification. We conducted seven plan-do-study-act cycles and evaluated the scores at seven subsequent data abstraction time points from November 2021 to December 2022, reviewing 404 patients (199 BR; 114 CRC; 91 NSCLC). All measures were improved. Four months after the intervention, OQS surpassed the quality threshold and was maintained for 10 months until the end of the study (range, 78-87% for QCP Track measures; 78-86% for combined measures).

CONCLUSIONS

We developed an easy-to-implement intervention that achieved a fast improvement in OQS, enabling our Medical Oncology Department to aim for QOPI certification.

Additional Value From Free-Text Diagnoses in Electronic Health Records: Hybrid Dictionary and Machine Learning Classification Study

BACKGROUND

Physicians are hesitant to forgo the opportunity of entering unstructured clinical notes for structured data entry in electronic health records. Does free text increase informational value in comparison with structured data?

OBJECTIVE

This study aims to compare information from unstructured text-based chief complaints harvested and processed by a natural language processing (NLP) algorithm with clinician-entered structured diagnoses in terms of their potential utility for automated improvement of patient workflows.

METHODS

Electronic health records of 293,298 patient visits at the emergency department of a Swiss university hospital from January 2014 to October 2021 were analyzed. Using emergency department overcrowding as a case in point, we compared supervised NLP-based keyword dictionaries of symptom clusters from unstructured clinical notes and clinician-entered chief complaints from a structured drop-down menu with the following 2 outcomes: hospitalization and high Emergency Severity Index (ESI) score.

RESULTS

Of 12 symptom clusters, the NLP cluster was substantial in predicting hospitalization in 11 (92%) clusters; 8 (67%) clusters remained significant even after controlling for the cluster of clinician-determined chief complaints in the model. All 12 NLP symptom clusters were significant in predicting a low ESI score, of which 9 (75%) remained significant when controlling for clinician-determined chief complaints. The correlation between NLP clusters and chief complaints was low (r=-0.04 to 0.6), indicating complementarity of information.

CONCLUSIONS

The NLP-derived features and clinicians' knowledge were complementary in explaining patient outcome heterogeneity. They can provide an efficient approach to patient flow management, for example, in an emergency medicine setting. We further demonstrated the feasibility of creating extensive and precise keyword dictionaries with NLP by medical experts without requiring programming knowledge. Using the dictionary, we could classify short and unstructured clinical texts into diagnostic categories defined by the clinician.

Establishing an effective clinical data collecting tool for optimal evaluation of native and allograft renal biopsies

Introduction

Percutaneous kidney biopsy is the gold standard method to reach a precise diagnosis in most medical kidney diseases, which positively impacts patient care by personalizing the treatment. Accurate diagnosis in the pathology report for medical kidney diseases requires clinicopathological correlation, and clinical data is not always reachable to the nephropathologist. This study aimed to create a standardized, paperless requisition form compatible with medical renal biopsies.

Methods

An initial form was prepared for native and allograft renal biopsies according to the current classification of medical kidney diseases. We invited 33 nephropathologists working in Canadian healthcare institutions to answer survey questions about the need to include a particular aspect of clinical information. According to the responses, we modified the experimental form. Eighty nephrologists were asked to complete a clinical data-collecting form given out as PDF files. The time for completing the form and clinicians' satisfaction were assessed.

Results

The experimental form survey was answered by 20 out of 33 nephropathologists (61%) from 14 Canadian healthcare centers. The agreement rate on the questions was from 38.89% to 100.00% (average 83.33% and 77.14% for the native and the allograft section, respectively). Seventeen out of 80 nephrologists and their assistants (21%) responded by completing 22 PDF forms. The time required to finish a PDF form was 10.4 min on average. Nephrologists considered the form time-consuming and suggested making it more clinically relevant. Only seven nephrologists responded to the satisfaction survey; four (57%) were satisfied.

Conclusions

Medical information is critical in renal pathology diagnoses. A uniform paperless clinical data requisition form was evolved through an agreement by Canadian nephropathologists.