System and method for optimizing home visit appointments and related travel
A system and a computer-implemented method employ an appointment optimization and route planning system (AORPS) for optimizing home-visit appointments and related travel for delivering patient care. The AORPS receives registration and patient data from patients and client input including information about healthcare providers, onsite care coordinators, health plans, appointment types, and success rates from a client. The AORPS collates the patient data and generates an input matrix from the client input and the collated patient data. The AORPS generates a predictive model for appointments, capitation, and return on investment for delivering patient care based on appointment and patient history, feedback, and healthcare data. The AORPS generates an appointment schedule with travel routes dynamically based on optimization factors derived from the client input, the collated patient data, the input matrix, the healthcare data, and the predictive model, incorporating real-time changes in patient data, the client input, the optimization factors, and appointments.
1. A system for optimizing home-visit appointments and related travel for delivering patient care, the system comprising:
one or more appointment scheduling modules;
a global navigation satellite system module;
a mapping module configured to receive location input from the global navigation satellite system module;
a non-transitory, computer-readable storage medium configured to store computer program instructions and data defined by an appointment optimization and route planning system;
at least one processor communicatively coupled to the non-transitory, computer-readable storage medium, the at least one processor configured to execute the defined computer program instructions; and
the appointment optimization and route planning system comprising:
a data reception module configured to receive registration data and patient data from patients;
the data reception module further configured to receive client input comprising hours of availability of healthcare providers and onsite care coordinators, appointment types, health plan commitments, and a success rate of operation, from a client;
an input processing module configured to collate the received patient data;
the input processing module further configured to generate an input matrix based on the received client input and the collated patient data, covering a preconfigured period of time, the input matrix comprising schedules of the healthcare providers and the onsite care coordinators, patient cohorts, expected minimum daily and maximum monthly volumes of appointments, and success rates of the appointments;
an output generation module configured to generate a predictive model based on training data comprising appointment history, patient history, feedback, and healthcare data; and
the output generation module further configured to generate an appointment schedule with travel routes dynamically via the mapping module using the global navigation satellite system module based on optimization factors derived from the received client input, the collated patient data, the generated input matrix, the healthcare data, and the generated predictive model, incorporating real-time changes in the patient data, the client input, the optimization factors, and appointments.
2. The system of claim 1 , further comprising a client device configured for use by the client to communicate with the one or more appointment scheduling modules via a network.
3. The system of claim 1 , wherein the output generation module of the appointment optimization and route planning system is configured to utilize one or more of decision trees, machine learning models, and regression models for generating and executing the predictive model and for generating the appointment schedule.
4. The system of claim 1 , wherein the received patient data comprises age, gender, profession, location, lists of chronic health conditions, medical history, healthcare programs enrolled by the patients, free-form self-reports about current health, reasons for requiring medical attention, preferred time bounds to interact with one or more of the healthcare providers and the onsite care coordinators, and payment information.
5. The system of claim 1 , wherein the data reception module is further configured to match input from the patients, while receiving the patient data, with standard ontologies of health-related information in real time to provide input suggestions to the patients while receiving the patient data.
6. The system of claim 1 , wherein the data reception module is further configured to receive the feedback in the training data from the patients, the client, the healthcare providers, and the onsite care coordinators.
7. The system of claim 1 , wherein the healthcare data comprises healthcare information of cohorts similar to the patients in terms of demographics and comorbidities, costs of healthcare implementations in a country, and insurance information.
8. The system of claim 1 , wherein the predictive model comprises predicted patient behaviors, suggested cadence of appointments for each of the patients, predicted equipment requirements, appointment cancellation probabilities for each of the patients, potential outcomes, expected costs, capitation projections for insurance plans, return on investment for the insurance plans, and clusters of patients based on each of the collated patient data, the client input, the feedback in the training data, social and psychosocial determinants of health of the patients, insurance plans of the patients, and insurance plan needs of the patients.
9. The system of claim 1 , wherein the output generation module of the appointment optimization and route planning system is further configured to assign a mode of appointment comprising one of a remote, high-touch patient examination mode and a telehealth mode to each of the appointments in the generated appointment schedule based on the optimization factors.
10. The system of claim 1 , wherein the output generation module of the appointment optimization and route planning system is further configured to dynamically adjust the generated appointment schedule with the travel routes in real time based on changes in the optimization factors, changes in the patients, changes in the healthcare providers and the onsite care coordinators, and rejection of the appointments by the patients with minimal disruption.
11. A computer-implemented method employing an appointment optimization and route planning system configured to define computer program instructions executable by at least one processor for optimizing home-visit appointments and related travel for delivering patient care, the method comprising:
receiving registration data and patient data from patients;
receiving client input comprising hours of availability of healthcare providers and onsite care coordinators, appointment types, health plan commitments, and a success rate of operation, from a client;
collating the received patient data;
generating an input matrix based on the received client input and the collated patient data, covering a preconfigured period of time, the input matrix comprising schedules of the healthcare providers and the onsite care coordinators, patient cohorts, expected minimum daily and maximum monthly volumes of appointments, and success rates of the appointments;
generating a predictive model based on training data comprising appointment history, patient history, feedback, and healthcare data; and
generating an appointment schedule with travel routes dynamically based on optimization factors derived from the received client input, the collated patient data, the generated input matrix, the healthcare data, and the generated predictive model, incorporating real-time changes in the patient data, the client input, the optimization factors, and appointments, via a mapping using a global navigation satellite system module.
12. The computer-implemented method of claim 11 , wherein the generation and execution of the predictive model and the generation of the appointment schedule is performed by utilizing one or more of decision trees, machine learning models, and regression models.
13. The computer-implemented method of claim 11 , wherein the received patient data comprises age, gender, profession, location, lists of chronic health conditions, medical history, healthcare programs enrolled by the patients, free-form self-reports about current health, reasons for requiring medical attention, preferred time bounds to interact with one or more of the healthcare providers and the onsite care coordinators, and payment information.
14. The computer-implemented method of claim 11 , further comprising matching input from the patients, while receiving the patient data, with standard ontologies of health-related information in real time to provide input suggestions to the patients while receiving the patient data.
15. The computer-implemented method of claim 11 , further comprising receiving the feedback in the training data from the patients, the client, the healthcare providers, and the onsite care coordinators.
16. The computer-implemented method of claim 11 , wherein the healthcare data comprises healthcare information of cohorts similar to the patients in terms of demographics and comorbidities, costs of healthcare implementations in a country, and insurance information.
17. The computer-implemented method of claim 11 , wherein the predictive model comprises predicted patient behaviors, suggested cadence of appointments for each of the patients, predicted equipment requirements, appointment cancellation probabilities for each of the patients, potential outcomes, expected costs, capitation projections for insurance plans, return on investment for the insurance plans, and clusters of patients based on each of the collated patient data, the client input, the feedback in the training data, social and psychosocial determinants of health of the patients, insurance plans of the patients, and insurance plan needs of the patients.
18. The computer-implemented method of claim 11 , further comprising assigning a mode of appointment comprising one of a remote, high-touch patient examination mode and a telehealth mode to each of the appointments in the generated appointment schedule based on the optimization factors.
19. The computer-implemented method of claim 11 , further comprising dynamically adjusting the generated appointment schedule with the travel routes in real time based on changes in the optimization factors, changes in the patients, changes in the healthcare providers and the onsite care coordinators, and rejection of the appointments by the patients with minimal disruption.
20. A non-transitory, computer-readable storage medium having embodied thereon, computer program codes comprising instructions executable by at least one processor for optimizing home-visit appointments and related travel for delivering patient care, the computer program codes comprising:
a first computer program code for receiving registration data and patient data from patients, wherein the patient data comprises age, gender, profession, location, lists of chronic health conditions, medical history, healthcare programs enrolled by the patients, free-form self-reports about current health, reasons for requiring medical attention, preferred time bounds to interact with one or more of the healthcare providers and the onsite care coordinators, and payment information;
a second computer program code for receiving client input comprising hours of availability of healthcare providers and onsite care coordinators, appointment types, health plan commitments, and a success rate of operation, from a client;
a third computer program code for collating the received patient data;
a fourth computer program code for generating an input matrix based on the received client input and the collated patient data, covering a preconfigured period of time, the input matrix comprising schedules of the healthcare providers and the onsite care coordinators, patient cohorts, expected minimum daily and maximum monthly volumes of appointments, and success rates of the appointments;
a fifth computer program code for generating a predictive model based on training data comprising appointment history, patient history, feedback, and healthcare data, wherein the healthcare data comprises healthcare information of cohorts similar to the patients in terms of demographics and comorbidities, costs of healthcare implementations in a country, and insurance information, and wherein the predictive model comprises predicted patient behaviors, suggested cadence of appointments for each of the patients, predicted equipment requirements, appointment cancellation probabilities for each of the patients, potential outcomes, expected costs, capitation projections for insurance plans, return on investment for the insurance plans, and clusters of patients based on each of the collated patient data, the client input, the feedback in the training data, social and psychosocial determinants of health of the patients, insurance plans of the patients, and insurance plan needs of the patients; and
a sixth computer program code for generating an appointment schedule with travel routes dynamically based on optimization factors derived from the received client input, the collated patient data, the generated input matrix, the healthcare data, and the generated predictive model, via a mapping using a global navigation satellite system module, incorporating real-time changes in the patient data, the client input, the optimization factors, and appointments.