HEALTHCARE professionals continue to face extraordinary pressure long after the pandemic ended. National surveys by the American Medical Association found that physician burnout peaked at 62.8% in 2021.

Although it has eased, physicians are still 82% more likely to experience burnout than workers in other fields. Another national survey reported that 77% of physicians spend longer hours on excessive documentation or complete paperwork at home.

Similar challenges are seen across Asia as hospitals struggle with staff shortages, long queues and healthcare workers who balance both medical and administrative duties.

The issue is not temporary. It reflects a deeper problem in how healthcare systems operate and how much time is lost to admin work that takes healthcare professionals away from patients.

One of Asia’s largest hospitals working with KewMann is already seeing practical gains from AI-assisted consultations. In a typical 15 to 30 minutes session, doctors previously spent much of their time typing notes and piecing together patient history.

With AI running quietly in the background, they can now focus fully on the patient while the system captures the conversation in real time. It identifies symptoms, history, assessments and plans, then produces a structured draft within seconds.

The AI traces insights and maps them to ICD-10-CM codes using medical corpora from the National Library of Medicine and the National Centre for Health Statistics across more than 50 disciplines. Doctors review and edit the summary in minutes. 

Documentation time drops by over 90% and consultations finish about 30% faster. Doctors also gain more complete records and feel less drained at the end of each day.

Staff rostering is a complex puzzle with many hard rules and soft preferences. A typical example for a general ward might look like this: each eight hours shift must include at least two senior nurses with speciality training, three general nurses, one floater who can cover unexpected tasks and one telemetry nurse for monitored patients.

Each staff member works one shift per day and is entitled to two rest days in a 14-day roster. There must be no PM to AM shift transitions for the same person and more. 

These rules make manual rostering time-consuming and error-prone, especially when last-minute changes occur. 

With AI, the system applies these constraints automatically and generates compliant rosters in minutes. When someone reports sick, it immediately proposes suitable replacements who meet the skill mix, workload limits and scheduling rules. Supervisors approve with one click. 

This reduces operational stress, cuts repetitive corrections and helps healthcare workers start each day with a stable, predictable shift plan.

Discharge summaries are another major source of administrative burden. A good discharge summary typically takes 30 minutes of a doctor’s time to prepare. A poor one can delay claims, confuse patients and force doctors to redo the document. 

With AI, healthcare workers can produce a full draft in less than one minute. The system compiles diagnoses, procedures, medication changes and follow-up needs by pulling information from multiple sources in the patient’s stay. Human intervention for routine and repetitive steps can reduce up to 80%.

Hospitals are also applying the similar technology to shift handovers. Instead of nurses and doctors spending valuable time compiling notes for the next shift, AI generates a complete care plan that summarises observations, alerts, risk changes and important updates. 

This ensures continuity of care, reduces miscommunication and removes hours of repeated manual entry. Healthcare workers describe it as a significant relief, especially after long shifts.

AI support tools should be treated as a necessary step for hospitals working to strengthen their workforce and improve care accessibility. The goal is not to replace healthcare workers but to protect them from the routine tasks that drain time and clarity. 

When AI manages documentation and administrative work in the background, healthcare workers can focus on the patient in front of them. Supporting their well-being ultimately protects care quality, national health outcomes and public trust. 

Kew Yoke Ling is the executive director of KewMann, an artificial intelligence and big data analytics company that leverages behavioural science to predict and influence human behaviour