Clinical Pathways for Oncological Gastrectomy: Are They a Suitable Instrument for Process Standardization to Improve Process and Outcome Quality for Patients Undergoing Gastrectomy? A Retrospective Cohort Study

Explaining Variability in Electronic Health Record Effort in Primary Care Ambulatory Encounters

BACKGROUND

 Electronic health record (EHR) user interface event logs are fast providing another perspective on the value and efficiency EHR technology brings to health care. Analysis of these detailed usage data has demonstrated their potential to identify EHR and clinical process design factors related to user efficiency, satisfaction, and burnout.

OBJECTIVE

 This study aimed to analyze the event log data across 26 different health systems to determine the variability of use of a single vendor's EHR based on four event log metrics, at the individual, practice group, and health system levels.

METHODS

 We obtained de-identified event log data recorded from June 1, 2018, to May 31, 2019, from 26 health systems' primary care physicians. We estimated the variability in total Active EHR Time, Documentation Time, Chart Review Time, and Ordering Time across health systems, practice groups, and individual physicians.

RESULTS

 In total, 5,444 physicians (Family Medicine: 3,042 and Internal Medicine: 2,422) provided care in a total of 2,285 different practices nested in 26 health systems. Health systems explain 1.29, 3.55, 3.45, and 3.30% of the total variability in Active Time, Documentation Time, Chart Review Time, and Ordering Time, respectively. Practice-level variability was estimated to be 7.96, 13.52, 8.39, and 5.57%, respectively, and individual physicians explained the largest proportion of the variability for those same outcomes 17.09, 27.49, 17.51, and 19.75%, respectively.

CONCLUSION

 The most variable physician EHR usage patterns occurs at the individual physician level and decreases as you move up to the practice and health system levels. This suggests that interventions to improve individual users' EHR usage efficiency may have the most potential impact compared with those directed at health system or practice levels.

Regional anesthesia prevents cancer recurrence after oncosurgery! What is wrong with the hypothesis?

Several studies have investigated the hypothesis of the efficacy of regional anesthesia (RA) techniques in preventing cancer recurrence when used perioperatively during oncological surgeries. Although theoretically, the association appears beneficial, the patient outcomes after cancer surgeries with or without RA were comparable, that is, the use of RA did not improve patient survival or prevent cancer recurrence after surgery. Another problem with this data is its retrospective nature which makes its interpretation difficult. Moreover, there are a lot of other confounding factors like comorbidities, tumor biology, nosocomial infections, duration of hospital stay, and baseline immunity, which is not comparable, and hence make standardization for a well-designed prospective study difficult. Return to intended oncologic therapy (RIOT) involves treatment in the form of radiation or chemotherapy which, if received on time after the planned oncosurgery, could provide a better chance of preventing cancer recurrence and improved survival. However, none of the retrospective studies have correlated cancer recurrence with delay in RIOT or not receiving RIOT as a cause of cancer recurrence. This paper discusses why even a well-designed, prospective trial could possibly never establish the efficacy of RA in preventing cancer recurrence and improving survival due to the complexities involved in a patient undergoing oncosurgery.

Multimodal Therapy of Upper Gastrointestinal Malignancies

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Improving management of hospitalised patients with COVID-19: algorithms and tools for implementation and measurement

BACKGROUND

The COVID-19 pandemic represents an unprecedented challenge to healthcare systems and nations across the world. Particularly challenging are the lack of agreed-upon management guidelines and variations in practice. Our hospital is a large, secondary-care government hospital in Kuwait, which has increased its capacity by approximately 28% to manage the care of patients with COVID-19. The surge in capacity has necessitated the redeployment of staff who are not well-trained to manage such conditions. There was a great need to develop a tool to help redeployed staff in decision-making for patients with COVID-19, a tool which could also be used for training.

METHODS

Based on the best available clinical knowledge and best practices, an eight member multidisciplinary group of clinical and quality experts undertook the development of a clinical algorithm-based toolkit to guide training and practice for the management of patients with COVID-19. The team followed Horabin and Lewis' seven-step approach in developing the algorithms and a five-step method in writing them. Moreover, we applied Rosenfeld et al's five points to each algorithm.

RESULTS

A set of seven clinical algorithms and one illustrative layout diagram were developed. The algorithms were augmented with documentation forms, data-collection online forms and spreadsheets and an indicators' reference sheet to guide implementation and performance measurement. The final version underwent several revisions and amendments prior to approval.

CONCLUSIONS

A large volume of published literature on the topic of COVID-19 pandemic was translated into a user-friendly, algorithm-based toolkit for the management of patients with COVID-19. This toolkit can be used for training and decision-making to improve the quality of care provided to patients with COVID-19.