Patient Analytics

OPD footfall is the starting point for every hospital planning decision: staffing, room allocation, equipment scheduling, and revenue forecasting all depend on knowing how many patients are arriving, when, and in which department. Yet most Indian hospitals still track OPD footfall in daily registers that get compiled into weekly or monthly summaries, making it impossible to catch intraday surges or department-wise imbalances until they cause a problem.

FireAI connects your OPD registration and HIS data to deliver live footfall dashboards at the department, doctor, and time-slot level. The system tracks walk-ins vs appointments, new vs returning patients, and footfall trends across days, weeks, and months so hospital administrators can staff departments proactively rather than reactively.

What FireAI tracks:

• Daily, weekly, and monthly OPD patient footfall by department and sub-specialty
• New patient vs returning patient split by department
• Walk-in vs scheduled appointment ratio and trends
• Peak hour analysis: which hours carry the highest patient load by department
• Day-of-week patterns to plan staffing and doctor schedules
• Department-wise footfall growth rate over rolling 30/60/90 day periods

Why opd patient analytics matter: A 200-bed hospital in Pune connected their OPD registration data to FireAI and discovered that their Orthopaedics department received 38% of total OPD footfall on Monday and Tuesday alone, while the remaining 5 days were underutilised. By adding one additional orthopaedic consultant on Monday mornings and redistributing scheduled appointments, they reduced Monday waiting times by 34% and improved Monday revenue per hour by 22%.

What you can ask FireAI:

• "Which departments had the highest OPD footfall last week?"
• "Show me hourly patient arrival patterns for Cardiology this month"
• "What is the new vs returning patient split for Gynaecology over the last 3 months?"

Patient visit frequency is one of the most underused metrics in Indian hospital analytics. A patient who visits once and never returns represents both a clinical risk and a revenue gap. A patient who returns regularly for follow-ups signals care continuity, treatment compliance, and long-term relationship value. Most hospitals cannot answer basic retention questions without custom database queries.

FireAI tracks individual patient visit histories across departments and builds retention cohorts automatically. The system flags patients who were due for follow-up but did not return, segments patients by visit frequency, and identifies departments with the highest and lowest retention rates so clinical leads can investigate root causes.

What FireAI tracks:

• Average visits per patient per month by department and doctor
• Patient retention rate: what share of first-visit patients return within 30/60/90 days
• Lapsed patient identification: patients with scheduled follow-ups who did not attend
• Visit frequency cohorts: one-time, occasional, regular, and frequent patient segments
• Department-level retention comparison to identify outliers
• Returning patient revenue contribution vs new patient revenue

How FireAI solves the retention visibility problem: A Chennai multi-specialty hospital used FireAI to track patient visit frequency across departments and found that their Endocrinology department had a 3-month retention rate of only 41%, far below the hospital average of 64%. Investigation revealed that diabetic patients were not receiving follow-up reminders after their first consultation. A structured follow-up call protocol for Endocrinology increased 3-month retention to 67% within two cycles, adding Rs 3.8 lakh per month in consultation revenue.

What you can ask FireAI:

• "What is the 60-day patient retention rate for each department this quarter?"
• "Show me patients who visited Cardiology once but have not returned in 90 days"
• "Which doctors have the highest patient return rate in Orthopaedics?"

Doctor consultation analytics is the bridge between patient demand and hospital revenue. If doctors spend too long per consultation, queues build and patients leave. If consultation times are too short, clinical outcomes and patient satisfaction suffer. Most Indian hospitals have no systematic view of consultation time by doctor, department, or time of day.

FireAI extracts consultation start and end timestamps from your HIS or appointment system and builds a doctor-level consultation analytics layer. It tracks average consultation duration, patients seen per hour, daily and weekly capacity utilisation, and time-in-clinic vs time-with-patients ratios so medical superintendents can identify capacity bottlenecks and optimise doctor scheduling.

What FireAI tracks:

• Average consultation time (minutes) by doctor, department, and time slot
• Patients seen per hour and per session by doctor
• Doctor capacity utilisation: actual consultations vs available slot capacity
• Consultation time variability: doctors with high variance in consultation duration cause unpredictable queues
• Revenue per doctor per session and per month
• Overtime analysis: sessions running beyond scheduled end times by doctor

How FireAI solves the doctor capacity problem: A Hyderabad multi-specialty hospital used FireAI's doctor consultation analytics to discover that their top revenue-generating cardiologist averaged 4.2 minutes per consultation, while the department average was 8.6 minutes. Post-consultation surveys showed this doctor's patient satisfaction score was the lowest in the department. A targeted scheduling adjustment that added 5 minutes per slot and reduced daily patient load by 4 immediately improved satisfaction scores by 18 points over the next 6 weeks.

What you can ask FireAI:

• "Which doctors had the highest consultation time this week?"
• "Show me capacity utilisation for all Orthopaedics consultants this month"
• "Which sessions consistently run overtime across all departments?"

Patient waiting time is the single metric most correlated with patient satisfaction in Indian hospitals. Long queues in OPD are the most common complaint in hospital feedback forms, and they directly affect repeat visits, referrals, and online ratings. Yet most hospitals track waiting time only through anecdotal feedback or periodic surveys, not from live data.

FireAI calculates waiting time from the gap between patient registration timestamp and consultation start timestamp in your HIS. It builds real-time queue dashboards that show current wait times by department, historical waiting time trends, and root cause breakdowns when waiting times spike so administrators can act before the queue becomes a patient experience crisis.

What FireAI tracks:

• Average waiting time (minutes) by department, time slot, and day of week
• Queue depth at any given time: number of patients registered but not yet seen
• Waiting time percentiles: P50, P75, P90 to understand how the long tail of waiting is distributed
• Wait time drivers: late doctor arrival, back-to-back emergency diversions, no-show gaps
• Correlation between waiting time and patient satisfaction scores where feedback data is connected
• Departments and time slots exceeding target waiting time thresholds

How FireAI solves the queue problem: A 350-bed hospital in Bengaluru used FireAI's patient waiting time analytics to find that 68% of OPD waiting time complaints came from three specific slots: Monday 10-12am General Medicine, Wednesday 11am-1pm Paediatrics, and Friday 9-11am Orthopaedics. All three slots had one thing in common: the primary consultant arrived an average of 22 minutes after their scheduled start time. A simple intervention of 15-minute buffer slots and a supervisor check-in at session start reduced average waiting time in those slots from 48 minutes to 26 minutes within 3 weeks.

What you can ask FireAI:

• "Which departments had the longest average waiting time this week?"
• "Show me waiting time trend for General Medicine over the last 3 months"
• "What percentage of OPD patients waited more than 45 minutes today?"