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How Data Science Can Reduce Patient Waiting Time

  • Highlights

  • Long patient wait hours in India

  • How data science can solve this problem

  • Streamlined operations

  • Better utilisation of existing infrastructure

According to a study conducted in a Tertiary Care Hospital in Pune in 2016, the average waiting time for a patient in the OPD was 60 minutes. Another international study on consulting time, published in medical journal BMJ Open says that the average time a doctor spends with a patient in India is a mere two minutes. This means that a patient waits an average of an hour for a consultation that lasts two minutes.

This state of affairs naturally reflects badly on the Indian healthcare system. However, new developments in data science can help greatly improve this situation. New hospital management software programs, basically apply predictive analysis to improve planning and execution of key care-delivery processes. Resource management is the chief among them. Hospitals and clinics can purchase and apply this software with professional business loans for doctors, to significantly reduce reduce patient waiting time, and not just in the OPD.

Here are a few ways that data science can reduce patient waiting time:

1. Increased OR Utilisation

One of the major benefits of data science in hospital management is the better and more efficient utilisation of the operation theatre (OR). Through predictive analysis, cloud computing, mobile technology, etc., it becomes a lot easier for the doctors themselves to block the OR and release it when their need is over. This allows for increased utilisation of the OR overall (and revenue through it). It also reduces the time that patients in need of surgeries have to wait.

2. Reduced Infusion Centre Wait Times

Infusion centres have a hard time scheduling appointment times. It is very difficult for even big centres to reduce their patient’s waiting period, especially during ‘the morning rush hour’. This is because there usually is a great deal of uncertainty with last-minute add-ons, late cancellations, no-shows, etc. However, applying predictive analysis and machine learning can optimise schedule templates, drastically reducing patient wait times.

3. Streamlining Emergency Department Operations

ED operations are quite famous for bottlenecks, with patients waiting for lab reports, or to get scans done, and so on. This maybe caused due to a lack of staff and a general lack of proper scheduling. Applying EHR management solutions can optimise the scheduling, finding the best order for events to take place. This will in turn reduce patient waiting times greatly and also make smarter use of the ED services. Costing around Rs.10 to 45 lakh, such solution can be effective in streamlining the emergency department operations and the cost of which can be easily financed by a business loan for doctors up to Rs.25 lakh.

How doctors can take their practice to the next level

Benefits of bajaj finserv business loans for doctors

Bajaj Finserv offers business loans to doctors up to Rs.35 lakh with added benefits like 24-hour disbursal, doorstep document pickup and Flexi Term Loan facility. With these special loans, you can integrate technology in your practice operations and streamline operations and boost profitability.

4. ED to In-Patient Bed Transfer

Predictive tools can show the likelihood of an ED patient being required to be admitted. It can also show the units that will be able to admit these patients. This makes the hospital’s and ED physician’s job a lot easier, in terms of on-boarding flow, prioritising which beds need to be cleaned first, which units should accelerate discharge and so on. This in turn reduces the patient’s waiting period to occupy a bed/ ward/ room.

Thus, if used correctly, data science solutions, coupled with right financing options like business loan for doctors for procurement and maintenance, can not only reduce patient waiting time, but can also lower health care costs, increase patient access, and better use the hospital infrastructure that’s already in place.

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