eReferral: implementing change to improve specialty accessMargot Kushel, MDAssociate Professor of Medicine in Residence

What is eReferral?

Electronic referral system for specialty care at San Francisco General Hospital

SFGH: public hospital of city/county of SF Operated by SFDPH Has longstanding affiliation with UCSF All physicians are UCSF faculty members, paid by UCSF via

affiliation agreement with CCSF Serves as cornerstone of Community Health Network Network of SFDPH funded community-based primary care

clinics, specialty hospital (SFGH) and LHH

Who receives specialty care at SFGH? under and uninsured patients

working poor socially chaotic (homeless, substance abuse) ethnic and racially diverse limited english proficiency chaotic social situations

Patients referred from CHN clinics, hospital based primary care clinics, community consortium clinics (FQHC)

Who provides specialty care at SFGH? UCSF faculty specialists, fellows,

residents Many clinicians who see patients for short

periods of times (fellows, residents) or for few clinic sessions a week

How are physicians reimbursed?

Salaried, paid by UCSF Clinical time is partially covered by

CCSF/UCSF affiliation agreement Under funds clinical time

A lot of time is “donated”

What were the problems?

Supply/demand mismatch Limited physician time to see large numbers of patients Long wait times

Poor communication Hand written referrals, free form

Hard to read, unclear question, got lost Triage by “hassle”

PCPs could page fellows to beg for earlier appointments Took everyone’s time Relied on personal connections which weren’t evenly distributed No rationale way to determine appointments

Many patients “overbooked” led to chaotic specialty clinic sessions Little communication back to PCP

No letters, chart notes handwritten, often lost

These data led to a pilot solution… Hal Yee MD PhD joined UCSF in 2005 to be

division chief of GI Recognized supply/demand mismatch

GI had wait time for new appointments of 11.5 months Did not have option to increase supply Recognized inefficiencies in system

Initiated GI eConsult with input from: IT, primary care providers, GI, and risk management

PCP submits electronic referral

Consult reviewed electronically by specialistIncludes all relevant clinical data from EMR

Appropriate specialty referralAND

Pre-referral work-up completePCP can manage with guidance

OR Pre-referral work-up incomplete

Schedule Next Available Overbook

Nonurgent Urgent

Not scheduledor more

information requested

Note in EMR

GI eConsult_______________________

GI eConsult: results____________________________

Next Available New Patient Appointment in GI (eReferral implemented 07/01/2005)

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But, it wasn’t just GI…____________________________

Clinic 2/4/05 4/22/05 2/21/06 5/22/06 8/16/06 11/8/06

Chest 55 97 121 129 148 120

Endocrine 59 157 226 231 232 204

Renal 301 223 241 228 310 184

Rheumatology 166 N/A 169 184 141 205

Hand 25 4 21 120 13 69

Ophthalmology 38 N/A 49 N/A 65 56

Optometry 55 101 48 112 121 152

Otolaryngology 14 N/A N/A 25 5 35

Urology 35 0 70 141 9 58

SFHP funds eReferral spread

three separate grants

• 4 medicine clinics

• 8 surgical clinics

• outpatient MRI, CT and U/S

project goals

• to decrease wait times

• to improve communication between specialists and primary care providers

____________________________

implementation schedule____________________________

JANCardiology Chest

Sleep Studies

FEB MARCH APRIL

MAYEndocrinology Rheumatology

JUNE JULYNeurosurgery

Orthopedics 5M Breast Clinic

AUG

SEPT OCT NOVPodiatry Urology

DEC

JANNeurology

Renal

FEB MARCH APRILPlastics

MAYENT

Hematology

JUNE JULY AUGUST

2007

2008

MedicineSurgery

RadiologyNot Grant Funded

Next: MRI, Diabetes, Obstetrics-Gynecology

eReferral evaluation

Made argument to the funders that we needed to fund evaluation

Decided to use evaluation as both evaluation and as feedback

As lead evaluator, worked alongside implementation team, attended all team meetings, provided input at all stages

What metrics?

Original pilot data looked at wait times Thinking about broader goals, hoped to

impact:Wait timesQuality of referralsEfficiency for both specialists and PCPsAcceptability

eReferral evaluation activities

Access and process measures eReferral database Time surveys of reviewers

Primary care provider survey (Yeuen Kim) Electronic questionnaire to all primary care providers (SFGH-based, CHN,

consortium clinic) Specialty Survey

Brief questionnaire Unit of analysis: new patient specialty visit (yes/no eReferral) 8 clinics; pre and post eReferral

Cost analyses (with RAND) Time costs Multiple interviews with key informants

Upcoming: (renal clinic focus) Proportion of new RRT patients whose first visit was 6 months prior to RRT

eReferral evaluation:eReferral database

Access indicatorsWait times

Process indicatorsProportion overbooked, regularly scheduled, not

initially scheduledProportion never scheduled

Payer mix/show ratesNo-show ratesPayer mix

Routinely scheduled appointments: wait times

Wait times for new appointments (routine) Feb 07- Feb 08: Medicine Clinics

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Endo (5/14/07 startdate)

Rheum (5/14/07 startdate)

Proportion of appointments never scheduled

% of eReferrals Never Scheduled: Medicine Clinics (for eReferrals submitted January-June 07)

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Proportion overbooked versus routinely scheduled (among those scheduled)

Clinic GI Cardiology Pulmonary Endo Rheum

Number of scheduled appointments 1810 865 379 204 324

Routine 1390 (77%) 581(67%) 297(78%) 114 (56%) 116 (36%)

Overbooked 420 (23%) 284 (33%) 82(22%) 88 (44%) 208 (64%)

Primary Care Survey

Electronic questionnaire to all PCPs in SFGH based, CHN and consortium primary care clinics that see adults

Asked about time spent doing eReferrals compared to paper methods, guidance of work-up, wait times, whether they thought it improved care overall

Gave room for free text as well Total n=368 Had 81% response rate without incentives Study n=298

Compared to prior methods….

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Overall clinical care

Ability to track referrals

Guiding pre-visit work-up

Answering clinical question

Wait time for new appt

Access for non-urgent issues

Access for urgent issues

Time spent submitting

Better No change Worse

In multivariate analysis…

. Providers from non-county-funded clinics (AOR 0.40, 95% CI 0.14-0.79) and those who spent ≥6 minutes submitting an electronic referral (AOR 0.33, 95%CI 0.18-0.61) were significantly less likely than other participants to report that electronic referrals had improved clinical care.

Kim et al. Under review

Specialty Survey

We created short (<2 minute) questionnaire to attach to patient charts in selected specialty clinics before and after eReferral

Asked first specialist to see patient to fill it out Asked about clarity of question, appropriateness of

visit, need for follow-up, could follow-up have been averted

Wanted to get specialists sense of eReferral: was this improving their ability to care for patients

Specialty Survey: n=450ish

Proportion of respondents for whom it was somewhat/very difficult to identify the

consultation/clinical question

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Medicine Surgical

% p

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Yes eReferral

No eReferral

Specialty Clinic Survey

Proportion of follow-up visit(s) that could have been avoided if WORK-UP HAD BEEN MORE COMPLETE

prior to visit (*Question asked only for patients for whom a follow-up appointment is scheduled)

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No eReferral

What role has evaluation played? Help define what metrics we should use to

measure success Provide mid-course corrections Provide data for business plan/funders to make

case for spread of eReferral Provide data to “nudge” constituents and

powers that be i.e. issue of dictating notes

Overall, eReferral thought to be a success In clinics with most resource limitation, reduced

wait times Both PCPs and specialists believed it improved

communication Extra burden on PCP generally well tolerated Acceptance in part dependent on IT issues

Further from SFGH (IT wise), the less well accepted We have not evaluated patient outcomes and

acceptance

What were the factors that led to success? Motivated, coordinated effort

PCPs, specialists, IT, administration, funders Interest aligned with our payment system

In system that is “capitated,” everyone agrees on wanting to avoid “avoidable” appointments

Interactive evaluationAble to make mid-course corrections

What were factors that led to success? Effective team leadership, met regularly Used data to make mid course

corrections and relayed results back to stakeholders frequently

An idea whose time had come, which was well implemented…

My contact info…

Margot.Kushel@ucsf.edu