A typical workflow for booking an appointment can go like this:
1. Patient calls (or visits) the hospital, and speaks to the person at the reception, at a specific department
2. The person looks up the available time slots, that a doctor is free and available in the clinic
3. Consults with the Patient on the best time possible for her appointment and then schedules the appointment
Now this three step process can either happen on a call, at the hospital reception or via a website provided by the hospital. But in real life, the appointment booking process for a patient might not be so straight forward. Here are some of the different scenarios that might occur:
1. Doctor is not available, asks her medical assistant to cancel all her appointments. Existing appointments need to be shifted to other doctors or rescheduled based on patient priority.
2. Patient calls at the last moment and asks for her appointments to be re-scheduled or cancelled
3. Patient does not show up for the appointment, and asks for a new appointment
4. During a clinic day, multiple new and urgent cases need to be seen by the physician, which delay the subsequent appointments
5. Scheduling of renal therapy patients or cancer therapy patients also needs supervised scheduling that is closely related to the patients’ care protocols and care plans
6. Scheduling based on urgency and emergency situations also changes the “scheduled” visits of a doctor
Considering these challenges in the daily working environment of a hospital, an AI-based scheduling solution can help the hospitals in providing an optimal use of resources. For instance a research from Indiana University  found using Artificial Intelligence in patient care can be cost effective and improve patient outcomes.
Consider for instance the following statistics of a Government Hospital in Rajasthan, India :
- Nearly 1.27 crore patients were registered at OPD in medical centres affiliated to medical colleges and
- 9.27 crore in state medical institutions in the year 2014-2015
- in the year 2015 around 35,000 patients per day were registered at the OPD at medical college-affiliated centres
The High number of patients (the 35,000 per day patients registered at the OPD at medical college-affiliated centres) and the resource scheduling scenarios, presents an apt usecase to implement an AI based Appointment Scheduling system.
While it not only present a challenge to manage the care of all the visiting patients, it also allows for the administration to ask; How many doctors, nurses and medical assistants should be scheduled to manage the care planning & scheduling requirements of each of these patients, visiting one or many departments of the hospital.
In addition to Patient Scheduling, AI based algorithms can be deployed in such settings  to help the hospital administration in optimising the time of their most important resources: Physicians, nurses and medical assistants.
|Handbook of Healthcare System Scheduling – Reference |
Additional Scenarios where the AI based resource scheduling systems in Healthcare  can be deployed are:
- Operating Theatre + Operating Team Scheduling
- Renal Dialysis Centers
- Radiology Diagnostic Facilities
- Medication Reminders Apps
- Acuity-based nurse assignment and patient scheduling in oncology clinics
- Care Plans based activity & event scheduling
- Procedure Scheduling
- Health Checkups Packages
Once an AI based solution has been implemented, the scheduling, rescheduling, planning, allocating and many other scenarios are handled by an AI based Scheduling Agent allowing for hospital administrators and physician scheduling managers to focus on treating the patients.
And Scheduling a patient appointment becomes an autonomous process:
A. Jane emails Dr. John to schedule an appointment for a followup visit. Jane receives a confirmation email regarding the appointment with Dr. John from his assistant Amy. A reminder is set in her calendar.
B. Jane, on the day of the appointment is unable to make it to the hospital and sends an email requesting for rescheduling her appointment to the next wednesday. Amy reviews, Dr. Johns schedule and responds to Jane with a confirmation of her re-scheduled appointment.
In the above example Amy is an AI assistant to the Physician, nurse or medical health professional. Or in fact it could be an assistant (Siri, cortana, or amy from x.ai etc) to the Patient.
What do you think, do share your thoughts?
Sundar Pichai, CEO, Google says we are moving from a mobile first world to an AI first world, quite fast.
- How to use AI to automatically schedule your appointments with x.ai – TechRepublic http://ow.ly/lSdJ300yH9w
- [1206.1678] A Distributed Optimized Patient Scheduling using Partial Information http://ow.ly/u3A0300yHu3
- Artificial Intelligence in Healthcare: A Smart Decision? | Health Standards http://ow.ly/IR3N300yIep
- Can computers save health care? IU research shows lower costs, better outcomes: IU News Room: Indiana University http://ow.ly/bPWs300yIs6
- Association for the Advancement of Artificial Intelligence http://ow.ly/4aoc300yIxY
- E-registration Facility Soon At SMS HospitaleHEALTH | EHEALTH http://ow.ly/njMx300yJgz
- Handbook of Healthcare System Scheduling – http://ow.ly/cvUn300yLql
- From Scheduling Meetings To Shopping Deals: 14 Early-Stage AI Assistants To Watch http://ow.ly/R9b7301lqjK
- Who will turn out to be the better diagnostician? #digitalhealth #ArtificialIntelligence https://t.co/TmzInbDlg5
- Robot Takes On Role Of Hospital Scheduling Nurse | Digital Trends http://ow.ly/QTAW100eEgR
- This is how the future of hospital operations resembles air traffic control – MedCity NewsMedCity News http://ow.ly/BJh1100eIdv
- Can Artificial Intelligence Help The Mentally Ill? https://t.co/e5NEnYOpAL #mentalhealth #AI
- On-line Appointment Sequencing and Scheduling – Brian Denton et al, http://ow.ly/RXXm300yLHX
- Artificial Intelligence Can Improve Healthcare | EMR and EHR http://ow.ly/MlBy302ur9Q
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