Mary Greeley Medical Center: Patient Transport Quality Improvement Colten Fales, Shane Goodall, Michael Hoefer, Steve Quarnstrom
Introduction • Mary Greeley Medical Center is a 220-bed medical center serving a 13 county region in central Iowa. • Mission Statement: “We provide high quality, cost effective health care services that advance the health of central Iowans through specialized care and personal touch.”
Patient Transport Process • Patients staying in the hospital need transportation to and from various appointments. • “Transporters” are dispatched to pick up and drop off patients • Transport delivery system(TDSS) handles transport requests
Problem Description • Patients arriving late to appointments • Patients left unattended • Transporters’ time wasted • Current data collection doesn’t show full picture • Data Accuracy has not been tested • Extent of problems widely unknown
Solution Plan • Time study to learn more about specific process elements • Verify TDSS accuracy against “gold-standard” hand timings • Characterize high-variability elements, including root cause • Develop recommendations to improve data quality and reduce transport cycle time, improving patient care
Data Collection • 20 hours of observed time • 44 transports observed • 11 times recorded per transport • Qualitative notes
How accurate are the measured times? • TDSS measured times are, on average, one minute and 45 seconds slower than the hand timings. • Hand measurement error may have been a contributor • TDSS system measurement is close to the values measured by hand timings
When is the transport actually complete? • Each patient arrives 3 minutes before the transport is recorded as “complete” in TDSS • Reasons: • Not using closest phone to complete call • Other non-value added walking time
Transport Process Distribution • Quantitative look at TDSS data from January-April 2014 • Average transport time of 17 minutes for deliveries with no recorded delays from TDSS. • With consideration for the bias, average of 14 minutes. • Given the 15 minute time allotment for a transport, 44% of all transports will result in the patient arriving late to the appointment.
How Late are Patient Transports? • Quantitative look at TDSS data from January-April 2014 • After calibrating for the 3 minute bias, 75% of all patient deliveries from January-April were late by at least one minute. • Each appointment was, on average, 8 minutes and 30 seconds late.
Detailed Process Breakdown • 37% is value added time • 36% is unavoidable non value added time • 27% is non value added time spent waiting
Observed Wait Time Causes • Patient is meeting with doctor • Patient is busy eating, sleeping, etc. • No assistance is available to help transfer patient to stretcher • Patient is not finished with appointment • Etc.
Potential Solution #1 - Communication • Utilize SMS text messaging to alert nurses • If a patient cannot be readied in time, nurses can alert the transporter • Transporters can reallocate time to picking up another patient
Potential Solution #2 – Improved Data Collection • Using phones to measure times is ineffective and time consuming (takes 2% of total process time) • Electronic devices, such as PDAs or iPods, allow for easy data collection at various data points • Track transporter performance, allow for statistical process monitoring • Identify additional sources of waste
Potential Solution #3 – Cleaning on the go • 14% of process time is spent cleaning up or returning equipment • “Reuse” of current transport equipment without returning to a common dock can save time • Cleaning supplies can be attached to each transport equipment allowing for cleaning “on-the-go”
Potential Solution #4 – Increase allowed transport time • 15 minute buffer is often not enough time to complete the transport • Increase buffer to ensure higher on-time delivery rates
Potential Solution #5 – Implement a location based optimization system • Track transporters via GPS or RFID • Intelligently dispatch transporters to certain patients depending on proximity to patient and availability • Improves data collection process: • More frequent • More accurate • Automatic • Relatively expensive investment
Vision for the Future • Reduction in transporter waiting time • Overall decrease in transport cycle time • Fewer late transports • Enhanced data collection • Robust process monitoring and intervention • Improved patient satisfaction