Ideation modeling for optimizing medical workflow in chronic patient management and nursing: advantages of artificial intelligence

Aim:
Prevention and treatment of chronic pathologies improve health at the population level and reduce socioeconomic costs. This study evaluates the effectiveness of several ideation models, with particular attentiveness to implementing artificial intelligence (AI), for optimizing workflow in a single hospital ward.

Material and methods:
Models included the processing of medical documentation, treatment, and personnel and service costs referring to patients with chronic musculoskeletal disorders or trauma. Nine sub-models were considered including average, increased, or reduced volumes of medical files, each with full or partial use of AI, and the lack thereof. The following assumptions were made: 1/ 20-bed ward employing six doctors, 12 nurses, and any number of non-medical secretaries for documentation processing; 2/ doctors and nurses working for 168 hours/month, with 80% of worktime excluding official breaks and holidays; 3/ hourly staff remuneration conformed to the Agency for Health Technology Assessment and the Tariff System in Poland.

Results:
The modeling showed that the bigger the number of patients, the higher the revenue, the smaller the total costs, and the higher the earnings margin. It also showed that the most efficient and least pricey, with more than three-fold savings, scenario referred to reduced documentation volume fully dealt with AI.

Conclusions:
We conclude that the introduction of AI into medical file processing benefits health workers' productivity in the workplace and reduces the socioeconomic strain of health care.