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INTRODUCTION: Burnout is defined as a prolonged state of physical and psychological exhaustion. Doctors, due to the demanding nature of their job, are susceptible to facing burnout, which has far reaching implications on their productivity and motivation. It affects the quality of care they provide to patients, thus eroding the doctor-patient relationship which embodies patient centeredness and autonomy. The study aims at addressing the stressors leading to burnout and its effect on the doctor-patient relationship. METHODS: A descriptive, cross-sectional study design with convenience (non-probability) sampling technique was employed in six major hospitals of Lahore, Pakistan. A total of 600 doctors were approached for the study which included house officers or "HOs" (recent graduates doing their 1 year long internship) and post-graduate trainees or "PGRs" (residents for 4-5 years in their specialties). Burnout was measured using the Copenhagen Burnout Inventor (CBI) while attitudes towards the doctor-patient relationship was measured using the Patient Practitioner Orientation Scale (PPOS), which measures two components of the relationship: power sharing and patient caring. Pearson correlation and linear regression analysis were used to analyze the data via SPSS v.21. RESULTS: A total of 515 doctors consented to take part in the study (response rate 85.83%). The final sample consisted of 487 doctors. The burnout score was not associated with the total and caring domain scores of PPOS (P > 0.05). However, it was associated with the power sharing sub-scale of PPOS. Multiple linear regression analysis yielded a significant model, by virtue of which CBI scores were positively associated with factors such as female gender, feeling of burn out, scoring high on sharing domain of PPOS and a lack of personal control while CBI scores were negatively associated with private medical college education, having a significant other, accommodation away from home and a sense of never ending competition. Burnout levels varied significantly between house officers and post graduate trainees. Twenty-three percent of the participants (mostly house officers) had high/very high burnout levels on the CBI (Kristenson's burnout scoring). Both groups showed significant differences with respect to working hours, smoking status and income. CONCLUSION: Although burnout showed no significant association with total and caring domain scores of PPOS (scale used to assess doctor-patient relationship), it showed a significant association with the power sharing domain of PPOS suggesting some impact on the overall delivery of patient care. Thus, it necessitates the monitoring of stressors in order to provide an atmosphere where patient autonomy can be practiced.
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Objective To predict changes in the quality of life scores of hemodialysis patients for the coming month and the development of an early warning system using machine learning Methods It was a prospective cohort study (one-month duration) at the dialysis center of a tertiary care hospital in Pakistan. The study started on 1st October 2016. About 78 patients have been enrolled till now. Bachelor of Medicine and Bachelor of Surgery (MBBS) qualified doctors administered a proforma with demographics and the validated Urdu version of World Health Organization Quality Of Life-BREF (WHOQOL-BREF). It was to be repeated after one month to the same patient by the same investigator. Simple statistics were computed using SPSS version 24 (IBM Corp., Armonk, NY) while machine learning was performed using R (version 3.0) and Orange (version 3.1). Results Using machine learning algorithms, two models (classification tree and Naïve Bayes) were generated to predict an increase or decrease of 5% in a patient's WHOQOL-BREF score over one month. The classification tree was selected as the most accurate model with an area under curve (AUC) of 83.3% (accuracy: 81.9%) for the prediction of 5% increase in QOL and an AUC of 76.2% (accuracy: 81.8%) for the prediction of 5% decrease in QOL over the coming month. The factors associated with an increase of QOL by 5% or more over the next month included younger age (<19 years) and higher iron sucrose doses (>278mg/month). Drops in psychological, physical, and social domain scores lead to a decrease of 5% or more in QOL scores over the following month. Conclusion An early warning system, dialysis data interpretation for algorithmic-prediction on quality of life (DIAL) was built for the early detection of deteriorating QOL scores in the hemodialysis population using machine learning algorithms. The model pointed out that working on psychological and environmental domains, in particular, may prevent the drop in QOL scores from occurring. DIAL, if implemented on a larger scale, is expected to help patients in terms of ensuring a better QOL and in reducing the financial burden in the long term.
