The Future of Patient Scheduling and Case Management Saúde
HRDSS SUMMIT AGENDA Project Overview Primary Stakeholders Hospitals Doctors Office Project Implementation Plan Time Plan Product Design External Resources Algorithms Alert System Schedule Validation and System Tuning
Project Overview 3 System HRDSS– Healthcare Decision Support HIBI– Healthcare Information Business Intelligence HABPE – Healthcare Analytics Business Processing Engine Saúde Healthcare in Galician 3 Main Product Offerings with in Saúde HRDSS – Q2 2010 The Decision Support System Taking Contracts now HIBI – Q3 2010 Provides day to day insight and transforms data into actionable insight HABPE – Q1 2010 Performs analytic processing for both HIBI and HDRSS.
Project Overview Manage 3 KPI’s AWT – Average Wait Time PAT – Physician Availability Time PPP – Profitability Per Patient HRDSS Health Resources Decision Support System Provides end to end solution for effective DSS to positively affect the patient experience and bottom line through minimizing patient wait times and maximizing provider availability
Project Overview Manage 3 KPI’s AWT – Average Wait Time PAT – Physician Availability Time PPP – Profitability Per Patient HRDSS Interface with various EMR and HER systems allowing a lower entry cost thereby lowering the barrier to entry for facilities of any size. Support for best of breed add-on modules as well as open source models Open Source System
Benefits of HRDSS Manage 3 KPI’s AWT – Average Wait Time PAT – Physician Availability Time PPP – Profitability Per Patient Benefits of HRDS Reduction in patient wait times Maximize billable resources Improve customer service Reduce cost overruns due to poor inventory management Administrators will be able to see snapshots of hospital utilization at any given time and pull from historical data as well Minimize unnecessary charges to patients by integrating with existing diagnostic systems Provide window to administrators for future planning of resources due to historical data and hypothetical situations Ultimately HRDSS will help administrators determine the best allocation of resources such as scheduling of patients, the flow of patients from one Medical Unit to another, directing the patients for true emergency to the ER and less life threatening cases to maybe nearby Urgent Care Centers; thus minimizing patient queues.
Project Implementation Schedule meeting between Planning Team (client) and Solutions Team (Saude) Develop Project timeline Establish Project Scope Identify Existing Critical Software Integration Needs Design Phase Establish User Requirements Gather Existing Data Elements Testing Phase Evaluate and Test against user requirements Add and correct errors found in testing Implementation Phase Go-live with user groups identified in project timeline Identify bugs and submit to developers for patches
Primary Stakeholders Each class may contain several subclasses with stakeholders of varying degree. The following stakeholders will benefit from the use of HRDS: Hospital Administration Clinical Users Medical Staff Hospital Administration This user may be cost-driven. Applications are highly specialized and productivity is high, as is the business cost of downtime and application inefficiency. Investment in capital costs is high for such users (e.g., Administrators, CxO’s, Accounting). Clinical Users The clinical worker uses IT to collect data from many sources, adds considerable value to that data by converting it into information, and communicates information, creating knowledge in support of a decision-making transaction. Medical Staff This user will be similar to the clinical user, although less dependent on the system to make decisions related to resource management.
Product Designs System Design Data Analysis and Flows The DSS will give the most optimal solution at the given point in time. Once the patient is being processed, the next new arrivals will utilize this data. Also, the optimization process should be adjustable. In the case scheduling in the Non-Appointment department like Emergency Room, the system will be using random times, random patients at each given time. At each decision point, when a new patient is submitted, the system is “frozen”, the best scheduling technique is chosen at a given point in time. Then, the heuristics are performed to provide the best sequencing that patients should be selected, so the objective function is minimized.
Product Designs DATA ELEMENTS ENTITY TYPE (subclasses - Patient, Nurse, Physician, Laboratory Technician) PATIENT (Patient#, LastName, FirstName, Arrival Time, Address Phone Number, Processing Time) Inpatient - hospitalized patients Room Number Number of Days in the Hospital Due Dates Previous Medical Information Tracking of Patient Care Outpatient – patients that come in on ambulatory basis (A patient who is admitted to a hospital or clinic for treatment that does not require an overnight stay.) Arrival Date Location where Diagnosis is given Processing Priority Transportation Need Previous Medical Information Upcoming Appointments Track Patient Care Follow-ups Preventative Care Emergency Patient - patients currently in the ER waiting and being diagnosed Previous Medical Information Arrival Time Diagnosis Processing Priority Resources Required for the patient for processing
Product Designs DATA Needs External Systems Can interface with any EMR, EHR, and Patient Management System DEPARTMENT (Business Unit) Department (# Rooms, # of beds/chairs, servers per room, Appointment Type) - Rooms Example: patient rooms, surgery rooms, radiology rooms
SCHEDULING ENVIRONMENT (Appointment Type) - Appointment Based Example: Doctor’s Offices - Non-Appointment Based Example: Emergency Center, Radiology Center, Laboratory Services
Product Designs DATA ELEMENTS ENTITY TYPE (subclasses - Patient, Nurse, Physician, Laboratory Technician) NURSE (Name, LastName, Department, Shift)
INPUT HABPE OUTPUT # Patients Waiting Processing Time Arrival Time Processing Priority # Rooms / Chairs available Waiting Time Minimized Example: Based on the input, patient X is the patient that should be selected first
Layer 1 Decision Point 1 Decision Point 2 Decision Point 3 Layer 2 Optimization k Optimization k + 1 Optimization k + 2
Patient Scheduling Model Patient Scheduling in the Department (Non-appointment based, stochastic) Methodology Monte Carlo Simulation Method may be used for mathematical optimization Instances: Emergency Center Radiology Center Laboratory Services Patient Scheduling in the Department (Appointment based, deterministic) Methodology Queuing Theory 0-1 Mixed Integer LP, Generic Search Instances: Doctor’s Office Surgery Room Patient Admission and Discharge Food Service Nuclear Medicine Room
Appointment - Based Medical Staff (Physicians, Nurses, etc) Review Current and Upcoming Patient Appointments View Patient Treatment History and Data View Patient’s Processing Times View Staff Schedule Submit and Modify Patient release information View which patient to process next Modify Patient Processing Priority Clinical Users Submit new Patient Information Schedule a new patient Review Current and Upcoming Patient Appt. View Current Medical Staff Schedule View Current Resource Availability Review Patient Processing Times Modify Patient Processing Priority APPL ICATION UI Non-Appointment - Based Medical Staff (Physicians, Nurses, etc) Review Current and Upcoming Patient Appointments View Patient Treatment History and Data View Patient’s Processing Times View Staff Schedule Submit and Modify Patient release information View which patient to process next Modify Patient Processing Priority Hospital Administration Review Current Medical Staff Schedule Analyze Patient Processing Time and Performance Maintain Medical Staff Information Submit new Medical Staff Schedule Add new Hospital Resources Assign servers per room/bed Maintain system Parameters