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Intragumtornchai T ,
Pannarunothai S ,
Laohavinij S ,
Patjanasoontorn B ,
Suntorntham S ,
Krittayaphong R ,
Bowonwatanuwong C Objective: To estimate the number of the ten subspecialists in internal medicine (neurologist, cardiologist, endocrinologist, gastroenterologist and hepatologist, nephrologist, hematologist, oncologist, rheumatologist, pulmonologist, and infectious disease specialist) needed for complex medical care based on the workload in the year 2013.
Material and Method: The present study applied a needs assessment model with evidence-based approach. Claimed data of inpatients in the year 2013 from the three government insurance schemes (the Civil Servant Medical Benefit, the Social Security and the Universal Health Coverage schemes), and out-patient data from Universal Coverage System were used to estimate demand for subspecialists. The Human Resource Working Group of the Royal College of Physicians of Thailand agreed on the conceptual framework to estimate the need for ten subspecialists based on clinical activities of outpatient consultations, inpatient ward rounds and non-operating room procedures on medical cases of respective diagnosis related group with severe and catastrophic comorbidities and complications by the Thai-DRG version 5. Representatives from the Associations of IM subspecialties approved the lists of ICD-10 diagnosis and ICD-9-CM procedure codes specific to each subspecialist care and proposed assumptions on rates of consultations from other specialists. Surveys were done to subspecialists in 6 major provincial clusters and representatives from IM subspecialty Associations asking time spent on main activities of patient care. The number of full-time-equivalent (FTE) subspecialists needed was calculated by multiplying the clinical workloads measured in minutes spent for each activity (ward round, ward work, inpatient and outpatient consultations) to get the total time needed, then divided by the available time for clinical activity of one subspecialist.
Results: From 5.9 million inpatient discharges in the year 2013, primary responsibility of patients in respective severe and catastrophic DRGs related to specific subspecialist workloads were summed up for teaching hospitals and regional hospitals ranging from as lowest the 2,849 cases for rheumatology to the highest 24,610 cases for gastroenterology and hepatology. The number of inpatient non-operating room procedures by ICD-9-CM as listed by IM subspecialty Associations ranged from 8 times for endocrinologists to 22,927 times for cardiologists for the whole year. Of ten subspecialists, the estimated numbers of cardiologist, nephrologist, neurologist, gastroenterologist and hepatologist, endocrinologist, oncologist, rheumatologist, hematologist, pulmonologist and infectious disease subspecialist needed at teaching and regional hospitals were 516, 241; 345, 144; 312, 143; 195, 124; 189, 45; 137, 170; 90, 47; 96, 111; 203, 87 and; 129, 44 respectively according to the workload recorded in the year 2013. The forecast FTE found the overall gap of discrepancy at 7 percent. If the distributions of these subspecialists in public and private hospitals were taken into account, the gap of discrepancy in public hospitals increased to 47 percent.
Conclusion: The demand-based forecast for the number of subspecialist needed was made possible with assumptions on conceptual framework for case selection, the rates of consultation and time-spent related to activities of patient care. The estimated numbers of subspecialists were anticipated far from optimum since the workload in the year 2013 was derived as a consequence of pre-existing suboptimal infrastructure of healthcare system. In addition, the deficit of subspecialists may increase in the near future when highly efficient, non- or mildly invasive, time-consuming procedures of acute illness increase. Sustainable matching demand and supply of human resource for health needed further validations of these assumptions.
Keywords: Internal Medicine subspecialists, Internal Medicine subspecialty, DRG database, demand-based forecast, human resource for healthcare system