Improving Access to Care through Decision Support Algorithms

ATTN: Business and company leaders whom aspire to excel,

Meet Freddie Weiss (B.S. industrial and systems engineering, Georgia Tech), Beth Garcia, Anita Ying, M.D., Laura Burke and Gwen Tate

MD Anderson Cancer Center

Improving Access to Care through Decision Support Algorithms

The University of Texas MD Anderson Cancer Center, based out of Houston, Texas, provides cancer patient care, research, education and prevention.  MD Anderson has been ranked the leading cancer hospital for 11 of the past 14 years by the U.S. News & World Report’s “Best Hospitals” survey.  The hospital provides over 1 million outpatient clinic visits, treatments and procedures each year.  In FY15, the hospital processed approximately 68,000 new patient referrals with 39,000 new patients seen.

The goal of this project was to improve the patient experience through ease of access and to reduce the time between initial contact and the scheduling of a new patient appointment where appropriate. Previously, an appointment could not be offered to a patient until medical records were received and reviewed by the clinical team to determine if it was medically appropriate for the patient to come to MD Anderson and if yes, which service and provider the patient should see.  This process could take anywhere from 3 to 15 days.  Data on cancellation rates showed that the top reasons for cancellation were “Unknown,” “Shopping for Options,” and “In Treatment Elsewhere.”  Although there were some inconsistencies with how the cancellations were labelled, these reasons pointed to the same issue.  By the time the staff member contacted the patient to schedule the appointment, many times the patient had already elected to seek treatment elsewhere.  Historically, the hospital had a 35%-40% cancellation rate.

The team used a systems engineering approach to evaluate the new patient process.  The team evaluated the process using a process map to identify key areas for improvement and focused on the desire to provide a patient with a new patient appointment during the initial call.  A pilot study was conducted in the Endocrine Center to establish proof of concept.  As the access staff were not clinical personnel, an algorithm (or decision tree) was needed to aid the staff in determining when an appointment could be given without additional medical review and with which service/provider the patient should be scheduled.  The team then worked with the provider team to document the clinical decisions made when reviewing medical records. Algorithms were designed to allow access staff to gather relevant medical information from the patient.  Where appropriate, the staff could then schedule a new patient appointment for patients who did not require a more detailed review.  When developing the questions for the algorithms, the medical staff focused questions on information that the patient would readily know, for example, “Have you had surgery to treat your cancer?” and “Have you been told that your cancer has spread to other parts of your body?”

Additional tools were implemented to enhance the benefits of the algorithms, including:

  1. Scheduling priority and timeframe which indicated the timeline for scheduling appointments (i.e. within 5 business days).
  2. Physicians’ diagnosis preference list that identified which physicians see which diagnoses.
  3. Terminology guide that defined which diagnoses were included in the algorithm and common terminology used by patients and referring providers (i.e. types of brain tumors).
  4. Standardized medical record request by diagnosis which outlined the medical records needed for the appointment (This document is sent to the referring physicians office).

These documents were combined with the algorithms into a toolkit for the access staff to use throughout the new patient process.  They were also used to train new employees to the process and measure performance.

The team monitored the percentage of patients that were given an appointment within one business day of referral initiation as well as the cancellation rate for new patient referrals.

After the implementation of the algorithm toolkit, the Endocrine Center saw an increase in the percentage of patients with an appointment created within one day of the referral to 69%.  The cancellation rate for new patient referrals to the center reduced from 53% to less than 15%.

Based on the results of the pilot study, the project scope expanded to include the remaining access centers. The institutional rollout began in November 2014 and as of May 2016, 10 centers have been completed and four are in progress. Preliminary data analysis has shown a similar trend with an increase in the percentage of patients with an appointment created within one day of the referral.  The team continues to monitor this metric as well as the referral cancellation rate. In addition to improving patient satisfaction and creating a more consistent experience, the project is expected to aid in staff training and retention, provide physicians with more time to focus on patient care, and help the institution capture a higher percentage of targeted patients.

Organization: The University of Texas MD Anderson Cancer Center

Team Members (L to R):

Freddie Weiss (B.S., ISE, Georgia Tech)
Healthcare Systems Engineer, Quality Measurement and Engineering

Beth Garcia
Director, Process Improvement & Quality Education

Anita Ying, M.D.
Associate Professor, Endocrine Neoplasia and HD

Laura Burke
Healthcare Systems Improvement Specialist, Quality Measurement and Engineering

Gwen Tate
Clinical Administrative Director, Brain & Spine Center

Industrial and System Engineers provide incredible value to any organization in any industry and I am really excited to share these stories and inspire you and your company to hire ISE’s.

Blessings to you all!

Best Regards,
Michael Foss
President, Institute of Industrial and Systems Engineering