Management of medical care capacity and hospitalization process with the use of digital technology at a specialized federal center

• Published in: Geniĭ ortopedii = Genij ortopedii Vol. 29; no. 2; pp. 127 - 136
• Main Authors: Kolyado, E.V., Peleganchuk, V.A., Povalikhin, A.N., Lazareva, V.V.
• Format: Journal Article
• Language: English
• Published: Russian Ilizarov Scientific Center for Restorative Traumatology and Orthopaedics 01.04.2023
• Subjects: capacity of medical care; hospitalizing management; information technology
• ISSN: 1028-4427; 2542-131X
• DOI: 10.18019/1028-4427-2023-29-2-127-136

Introduction Scheduling and distribution of medical care capacities approved by a healthcare organization, management of the key processes of patient selection and hospitalization with the use of digital technology are the most important organizational tools for successful implementation of state assignments. Purpose To develop an organizational model of medical care capacities, management of the processes of patient selection and hospitalization at a specialized federal center with the use of digital technology. Materials and methods Analysis of the plans of rendering specialized medical care in 72,547 cases, including high-tech medical care, for 10-year period (years 2013-2022) in the fields of “traumatology and orthopedics” and “neurosurgery” by means of healthcare information system and digital patients’ registries was conducted by the continuous method. Results Rates and types of patients’ nonappearance for hospitalization were identified: the rate of informed nonappearance was 37.9 ± 0.4 (per 100 planned patients), the rate of uninformed nonappearance was 18.4 ± 0.4 (per 100 patients who referred to admission), the rate of repeated nonappearance was 1.6 ± 0.1, and the rate of patients’ unplanned referral (arrival) was 6.0 ± 0.1. The rate of hospitalization rejection for a 10-year period (2013-2022) was 6.4 ± 0.1 (per 100 patients who sought medical care). For a 3-year period (2020-2022), the rate of non-confirmed surgical indications was 6.9 ± 0.1 (per 100 patients). Comparative analysis of 5-year periods (2013-2017 and 2018-2022) identified a 1.4-fold increase in an average 5-year rate of hospitalization refusal (t = 13.6, P < 0.0001). For a 5-year period (2018-2022), the rate of hospitalization of patients aged 75 and older was 5.3 ± 0.1, of patients with co-morbidity (diabetes mellitus) 11.8 ± 0.2 per 100 treated patients. Multi-purpose calculation method for prediction of patients’ hospitalization was offered. Discussion Based on the specified rate of patients’ nonappearance for hospitalization it would be advisable to provide a number of patients that would be over the plan in order to achieve necessary hospitalization numbers. An operating reserve in the digital patients’ registry would solve an issue of hospitalization plan execution and to substitute the patients who were not able to appear. Conclusion Our study has identified regularities of implementation of planned hospitalization of patients to traumatology/orthopedics and neurosurgery units in the conditions of a federal center.