220 likes | 349 Vues
Volume and Nature of Patient Telephone Calls in a Large Movement Disorders Center. O.R. Adam, MD; L.G. Aguilar Tabora, MD; J. Ferrara, MD; M. Nashatizadeh, MD; J. Jankovic, MD The Parkinson’s Disease Center and Movement Disorders Clinic Baylor College of Medicine Houston, Texas. Background.
E N D
Volume and Nature of Patient Telephone Calls in a Large Movement Disorders Center O.R. Adam, MD; L.G. Aguilar Tabora, MD; J. Ferrara, MD; M. Nashatizadeh, MD; J. Jankovic, MD The Parkinson’s Disease Center and Movement Disorders Clinic Baylor College of Medicine Houston, Texas
Background • Parkinson Disease Center and Movement Disorders Center (PDCMDC), Baylor College of Medicine, Houston, Texas • Established in 1977 – a total of 26,000 patients in database, averaging 1,500 new patients/year/last 5 years • 4 fellows rotate on “callbacks” one week at a time • On-call schedule for after hours and weekend patient calls
Background • One fellow at Columbia University Medical Center: • 263 new patients/year • 116 follow up patients/year • 15 inpatient consults/year • Patient care: 25 hours/week • Above volume and experience are comparable to that at the PDCMDC, BCM • Fellowship training in Movement Disorders • Clinic patients (new, follow-up) • Botulinum Toxin • DBS • Video rounds • Conferences and lectures • Movement Disorders Journal Club • Research Portera-Cailliau C, Victor D, Frucht S, Fahn S., Movement Disorders Fellowship Training Program at Columbia University Medical Center in 2001-2002; MovDisord 2005;21:479-85.
Background • Patient telephone calls are part of the movement disorders fellow training • “Mini” follow-ups providing the opportunity for the fellows to make independent decisions regarding the patient’s management • Movement disorders patients are unique
Objective • The nature, volume, and time allocation of patient-related telephone calls in a movement disorders center. • A secondary aim of this study was to determine the distribution of movement disorders diagnostic categories requiring callbacks.
Methods • Patient telephone calls received by movement disorders fellows and faculty physicians at the PDCMDC, Baylor College of Medicine • Period: 2 months (08/06/08 – 10/06/08) • Excluded: medication refills, pharmacy calls, study related questions, authorization and other health insurance related calls
Methods • Date • Time • Means • Caller • Demographics • Diagnosis • Date of last clinic visit • Number of medications and number of changes in the medications at the last visit • Reason and outcome of the telephone call • Telephone call duration
Results • Total number of callbacks: 633 • Total number of patients: 397 • Patient type: • New: 80 (20.15%) • Established: 317 (79.85%) • Mean age: 57.75 ± 21.45 • Average amount of time spent on the phone: • Per call: 6.59 min • Per day: 95 min (time recorded only by fellows)
Diagnosis DistributionCallback (CB) vs Clinic Sample *Two-sample t-test
One Time Callers (OC) vs Repeat Callers (RC) *two sample t-test
Is the Diagnosis Distribution of RC different from OC? *two-sample t-test
Characteristics OC vs RC *two-sample t-test
Call Reason Distribution per Diagnostic Groups (p<0.0001, Chi Square)
Conclusions • Patient telephone calls are time consuming: average 90 minutes/day/fellow • Patients who call more often and/or require longer conversations are: • PD (48%) • Atypical parkinsonism (5%) • Tourette syndrome (13%) • The main questions asked are: • Disease related (35%) • Medication related (21%) • Side effects (15%) • The main outcomes are: • Changes in medications (43%) • Counseling (21%) • Referral to the ER (1%) • 51% of calls were placed by someone other than the patient
Possible Solutions • Education at the initial visit (disease, prognosis, complications, treatment, medication side effects etc.) • Written educational hand-outs • Support groups • Internet education • Family education
Study Caveats • Short period: 2 months • Situation specific biases • Patient population bias • Cross sectional not longitudinal
Future Studies • Longitudinal • Methods to reduce the volume of callbacks • Assess the possible situation specific variation of callbacks distributed over particular diagnostic groups