important when considering how to structure and staff
an outpatient practice. Notably, nearly one-half of the
funding for these comparison practices was provided by
the institution with which they were affiliated.
Despite its limitations, we believe our survey to be the
first of its kind, offering developing palliative care programs a glimpse of existing outpatient practice structures, operations, and finances. This may be especially
useful for oncology programs and cancer centers, as palliative care is increasingly recognized as vital to comprehensive cancer care.6 Additional research is needed that
expands on this work to measure the clinical impact of
outpatient palliative care practices, financial viability, and
health system outcomes, including hospital and hospice admission rates.
Michael W. Rabow, MD
Alexander K. Smith, MD, MS, MPH
Janet L. Braun, RN, MSPH
David E. Weissman, MD
Author Affiliations: Department of Medicine, University of California, San Francisco (Dr Rabow); Department of Medicine, University of California, San Francisco and Veterans Affairs Medical Center, San Francisco
(Dr Smith); Palliative Care Center of the Bluegrass, Hospice of the Bluegrass, Lexington, Kentucky (Ms Braun);
and Department of Medicine, Medical College of Wisconsin, Froedtert Hospital, Milwaukee (Dr Weissman).
Correspondence: Dr Rabow, Department of Medicine,
UCSF Medical Center at Mount Zion, 1701 Divisadero
St, Ste 500, San Francisco, CA 94143-1732 (mrabow
@medicine.ucsf.edu).
Author Contributions: Dr Rabow had full access to all of
the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Rabow, Braun, and Weissman.
Acquisition of data: Rabow, Smith, Braun, and Weissman.
Analysis and interpretation of data: Rabow, Smith, and
Weissman. Drafting of the manuscript: Rabow and Smith.
Critical revision of the manuscript for important intellectual
content: Rabow, Braun, and Weissman. Statistical analysis: Rabow, Smith, Braun, and Weissman. Administrative,
technical, and material support: Rabow, Braun, and Weissman. Study supervision: Rabow and Weissman.
Financial Disclosure: None reported.
Funding/Support: Drs Rabow and Weissman and Ms
Braun have received support from the Center to Advance Palliative Care. No funding was sought or received for this research.
1. Morrison RS, Maroney-Galin C, Kralovec PD, Meier DE. The growth of palliative care programs in United States hospitals. J Palliat Med. 2005;8(6):
1127-1134.
2. Meier DE, Beresford L. Outpatient clinics are a new frontier for palliative care.
J Palliat Med. 2008;11(6):823-828.
3. Follwell M, Burman D, Le LW, et al. Phase II study of an outpatient palliative
care intervention in patients with metastatic cancer. J Clin Oncol. 2009;27
(2):206-213.
4. Rabow MW, Dibble SL, Pantilat SZ, McPhee SJ. The comprehensive care team:
a controlled trial of outpatient palliative medicine consultation. Arch Intern
Med. 2004;164(1):83-91.
5. Twaddle ML, Maxwell TL, Cassel JB, et al. Palliative care benchmarks from
academic medical centers. J Palliat Med. 2007;10(1):86-98.
6. Ferris FD, Bruera E, Cherny N, et al. Palliative cancer care a decade later: accomplishments, the need, next steps—from the American Society of Clinical
Oncology. J Clin Oncol. 2009;27(18):3052-3058.
HEALTH CARE REFORM
Electronic Medical Records and Upper
Extremity Symptoms: Pain With the Gain?
U
pper extremity musculoskeletal symptoms
(UEMSs) lead workplace injuries nationwide,1
incur the longest absence among service sector
workplace injuries (28 days median, exceeding fractures and amputations),2,3 and can foster multiple spells
of lost work time, so that work loss per episode underestimates the impact of UEMSs (by approximately 32%
at 5 years).4 Numerous studies link computer use to
UEMSs.3,5-8 However, to our knowledge, whether such
symptoms affect physicians practicing at facilities using
integrated electronic medical records (EMRs) has not been
assessed. We evaluated the prevalence of UEMSs attributed to computer workstation use in 2 medical facilities
with different EMR systems.
Methods. A cross-sectional survey assessed workrelated computer use and symptoms in primary care clinics at 2 academically affiliated centers in San Diego, California, with distinct EMRs. The US Department of
Veterans Affairs (VA) San Diego Healthcare System group
converted to the VA CPRS/VISTA EMR9 8 years prior to
the survey, and the University of California, San Diego
(UCSD) Medical group practice adopted the EpicCare
EMR 1 year prior to the survey. Both systems require extensive physician keyboarding and/or mouse clicks. Fiftynine physicians (87% of those targeted) completed the
20-minute survey. A visual analog scale (VAS) assessed
UEMSs attributed to computer use (scale, 0-100), validated against the QuickDash measure of UEMSs, not specific to computer use.10 Demographic variables reported
to predict UEMSs (age, sex, and body mass index [BMI]
components [weight and height])11 and computer use
(EMR-using clinical sessions per week and hours per week
of computer use) were elicited, as were institution, years
of computer use, and years of EMR use. Bivariable relations of dichotomized VAS scores to categorical variables were assessed by ␹2 analysis. Multivariable regression evaluated prediction of UEMSs (continuous VAS)
by computer variables, adjusted for UEM predictors of
age, sex, and BMI.11 A 2-sided ␣ level of .05 was used to
indicate statistical significance. This project was approved by the UCSD Human Research Protection Program & San Diego VA Research Compliance office.
Results. Moderate or severe reported UEMSs attributed
to computer use were common: 48% (29 of 59) reported VAS scores of 25 or higher. Mean (SD) VAS was
29 (25) (range, 0-100). The correlation of VAS to QuickDASH was 0.61 (P⬍ .001), supporting convergent and
construct validity for VAS.
Electronic medical record–using clinic sessions per
week (defined as half-days in clinic) ranged from 1 to 9.
A total of 18 clinicians (30%) had 5 or fewer clinic sessions per week, 8 of whom (44%) worked full time.
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A
100
UEMSs, VAS Score
80
60
40
20
0
0
2
4
6
8
10
80
100
EMR-Using Clinic Sessions per Week
B
100
UEMSs, VAS Score
80
60
40
20
0
0
20
40
60
Computer Hours per Week
Figure. Upper extremity musculoskeletal symptoms (UEMSs) (assessed on
a visual analog scale [VAS]) as a function of electronic medical record
(EMR)–using clinic sessions per week (A) and computer hours per week (B).
On bivariable analyses, higher VAS scores related to
more EMR-using clinic sessions per week (P=.02) and
more computer hours per week (P = .04).Other variables individually, including sex, age, and BMI, as well
as institution and years of EMR use and employment, bore
no significant relation to dichotomized VAS in this sample,
suggesting a more powerful relationship of EMR-using
clinic sessions or computer hours than other potential
UEMS predictors.
The prediction of UEMSs by computer hours per week
and particularly by EMR-using clinic sessions per week
was preserved or strengthened in analysis adjusted for
age, sex, and BMI (computer hours per week: ␤=13.9,
SE=6.48 [P=.04]; EMR-using clinic sessions per week:
␤ =4.30, SE=1.56 [P = .008]) (Figure).
Comment. High prevalence of UEMSs attributed to EMR
use was reported among physicians at 2 facilities with
integrated EMRs. Economic and personal losses arise irrespective of employment type, but “costs” from UEMS
disability are arguably amplified for physicians by the investment years of education required to practice medicine.
Our findings parallel those for nonphysician computer users: indeed, UEMSs are an emerging concern in
professional occupations.12 More than half of computer
users reported musculoskeletal symptoms during the first
year after starting a job.5 One review found that the most
consistent predictor was hours keying.3 However, mouse
use may more strongly predict UEMS development.6,13
The apparently stronger association to UEMSs of EMRusing clinic sessions per week (vs computer hours per
week) might be speculated to reflect relatively high mousing requirements of EMR systems during patientrelevant activities and/or reduced discretionary (nonclinic) computer use among those with UEMSs. Future
studies may evaluate whether modifications of EMR systems to minimize mouse use reduce the risk of clinic session–associated UEMSs.
The generalizability of findings must await replication in other settings; however, findings were consistent across 2 physician groups with distinct EMRs. Our
findings may underreflect the true impact, since prior injury and work modification were not captured and reduced clinic sessions engendered by UEMSs may produce strong bias to the null. (We are aware that at least
4 physicians received surgery, had clinic reductions,
and/or used specialized voice/foot pedal–operated workstations because of UEMSs.)
Ergonomics have received little attention in discussions of EMR implementation. (“Transitioning to an EMR
System Without Pain”14 refers to protection against figurative rather than literal discomfort.) This is perhaps ironic
because health care institutions are in the business of understanding risk factors for, and promoting prophylaxis
against, illness and injury.
To our knowledge, ours is the first assessment of this
issue for physicians. If high rates of UEMSs among physicians using EMRs are replicated and extend to other settings, the impact of physician symptoms and disability on
health-related quality of life, work time lost, patient care
continuity, and costs for health care institutions warrant
assessment. Proactive attention to ergonomics may be
merited, as integrated EMRs are increasingly adopted at
health care institutions.
These findings are timely, given the current efforts,
extending to the presidential level, to hasten nationalized implementation of EMRs.15,16
Beatrice A. Golomb, MD, PhD
Reza Yaghmai, MD, PhD
Marian J. Renvall, MS
Joe W. Ramsdell, MD
Author Affiliations: Departments of Medicine (Drs
Golomb, Yaghmai, and Ramsdell and Ms Renvall) and Family and Preventive Medicine (Dr Golomb), University of
California, San Diego, School of Medicine, San Diego.
Correspondence: Dr Golomb, Department of Medicine,
University of California, San Diego, 9500 Gilman Dr, MC
0995, La Jolla, CA 92093-0995 ([email protected]).
Author Contributions: Study concept and design: Yaghmai and Ramsdell. Acquisition of data: Yaghmai and Rams-
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dell. Analysis and interpretation of data: Golomb, Yaghmai, Renvall, and Ramsdell. Drafting of the manuscript:
Golomb and Renvall. Critical revision of the manuscript
for important intellectual content: Yaghmai, Ramsdell. Statistical analysis: Golomb, Yaghmai, and Renvall. Administrative, technical, and material support: Golomb and Ramsdell. Study supervision: Ramsdell.
Financial Disclosure: None reported.
Previous Presentations: A poster of this study was presented at the Society of General Internal Medicine annual
meeting, April 25-28, 2007; Toronto, Ontario, Canada.
Additional Contributions: Sabrina Koperski provided excellent editorial and administrative assistance. We thank
the physicians who kindly took the time to complete the
survey.
1. Punnett L, Wegman DH. Work-related musculoskeletal disorders: the epidemiologic evidence and the debate. J Electromyogr Kinesiol. 2004;14(1):
13-23.
2. Bureau of Labor Statistics. Nonfatal Occupational Injuries and Illnesses Requiring Days Away From Work, 2007. Washington, DC: Bureau of Labor Statistics: November 20, 2008 [reissued in March 2009].
3. Gerr F, Monteilh CP, Marcus M. Keyboard use and musculoskeletal outcomes among computer users. J Occup Rehabil. 2006;16(3):265-277.
4. Baldwin ML, Butler RJ. Upper extremity disorders in the workplace: costs
and outcomes beyond the first return to work. J Occup Rehabil. 2006;16
(3):303-323.
5. Gerr F, Marcus M, Ensor C, et al. A prospective study of computer users: I.
Study design and incidence of musculoskeletal symptoms and disorders. Am
J Ind Med. 2002;41(4):221-235.
6. Kryger AI, Andersen JH, Lassen CF, et al. Does computer use pose an occupational hazard for forearm pain; from the NUDATA study. Occup Environ Med. 2003;60(11):e14.
7. Gerr F, Marcus M, Monteilh C. Epidemiology of musculoskeletal disorders
among computer users: lesson learned from the role of posture and keyboard use. J Electromyogr Kinesiol. 2004;14(1):25-31.
8. Norman K, Nilsson T, Hagberg M, Tornqvist EW, Toomingas A. Working
conditions and health among female and male employees at a call center in
Sweden. Am J Ind Med. 2004;46(1):55-62.
9. Scalzi T. The VA leads the way in electronic innovations. Nursing. 2007;37
(9):26-27.
10. Gummesson C, Ward MM, Atroshi I. The shortened disabilities of the arm,
shoulder and hand questionnaire (QuickDASH): validity and reliability based
on responses within the full-length DASH. BMC Musculoskelet Disord. May
18 2006;7:44.
11. Becker J, Nora DB, Gomes I, et al. An evaluation of gender, obesity, age and
diabetes mellitus as risk factors for carpal tunnel syndrome. Clin Neurophysiol.
2002;113(9):1429-1434.
12. Griffiths KL, Mackey MG, Adamson BJ. The impact of a computerized work
environment on professional occupational groups and behavioural and physiological risk factors for musculoskeletal symptoms: a literature review. J Occup Rehabil. 2007;17(4):743-765.
13. Marcus M, Gerr F, Monteilh C, et al. A prospective study of computer users, II: postural risk factors for musculoskeletal symptoms and disorders.
Am J Ind Med. 2002;41(4):236-249.
14. Baum N, Jackson J, Drappi LG. Transitioning to an EMR system without pain.
J Med Pract Manage. 2007;23(1):16-18.
15. Shute N. How an electronic medical record can help keep your family healthy.
US News World Rep. March 2009:17.
16. Bhanoo S. Obama: all medical records computerized by 2014. The Industry
Standard. January 12, 2009.
COMMENTS AND OPINIONS
Importance of Distinguishing Supported
and Unsupported Off-label Drug Use
I
n their recent commentary, Largent et al1 propose
guidance to help clinicians distinguish between wellsupported and unsupported off-label prescribing.
In 2008, we published a related analysis using nationally representative data from a survey of office-based physicians to identify the frequency of unsupported off-
label use of specific therapies.2 We identified medications
for which research on off-label use is most urgently needed
based on recency of market entry, known adverse effects, high medication cost, high levels of promotional
expenditures, and high volume of uses with insufficient
evidence. Largent et al1 discussed these same features with
the exception of promotional efforts, a factor we consider crucial, given the potential impact of marketing on
prescribing. Among the top 25 prioritized medications,
antidepressants (escitalopram, trazodone, sertraline, bupropion, amitriptyline, and venlafaxine), antipsychotics (quetiapine and risperidone), and anxiolytics/
sedatives (zolpidem, lorazepam, and clonazepam) were
prominent. Given the complexities that clinicians face
in assessing the evidence base supporting different therapies,3 particular vigilance may be appropriate when using
these treatments.
An additional issue for consideration in off-label use
is the apparent tendency for clinicians to mistakenly apply evidence regarding one medication to others in the
same therapeutic class. Such inference is often not supported by good evidence.4
Off-label drug use occurs in diverse clinical situations, and many off-label uses are valid and well supported. We support Largent and colleagues in their classification of off-label uses as supported, suppositional,
and unsupported. Recognizing these distinctions will improve efforts toward better collection and use of evidence in prescribing. Given the volume of unsupported
off-label prescribing in the United States5 and the associated concern for lack of efficacy and potential harm,
further work is urgently needed to promote evidencebased prescribing.
Surrey M. Walton, PhD
Glen T. Schumock, PharmD, MBA
G. Caleb Alexander, MD, MS
David Meltzer, MD, PhD
Randall S. Stafford, MD, PhD
Author Affiliations: Departments of Pharmacy Administration (Dr Walton) and Pharmacy Practice (Dr
Schumock), College of Pharmacy, and Center for Pharmacoeconomic Research (Drs Walton and Schumock),
University of Illinois at Chicago; Section of General Internal Medicine, Department of Medicine, and Center for
Health and Social Sciences (Drs Alexander and Meltzer), MacLean Center for Clinical Medical Ethics (Dr Alexander), and Department of Economics (Dr Meltzer),
University of Chicago, Chicago, Illinois; and Stanford Prevention Research Center, Stanford University, Palo Alto,
California (Dr Stafford).
Correspondence: Dr Walton, Department of Pharmacy
Administration (M/C 871), College of Pharmacy, University of Illinois at Chicago, 833 S Wood St, Room 241,
Chicago, IL 60612-7231 ([email protected]).
1. Largent EA, Miller FG, Pearson SD. Going off-label without venturing off course:
evidence and ethical off-label prescribing. Arch Intern Med. 2009;169(19):
1745-1747.
2. Walton SM, Schumock GT, Lee K, Alexander GC, Meltzer D, Stafford RS.
Prioritizing future research on off-label prescribing: results of a quantitative
evaluation. Pharmacotherapy. 2008;28(12):1443-1452.
3. Chen DT, Wynia MK, Moloney RM, Alexander GC. US physician knowledge
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ing clinical decision making regarding treatment alternatives. We fully agree with the value of using decision
analysis to prioritize clinical scenarios most in need of further data and on the importance of basing decisions on patient-centered outcomes. Moreover, we support the authors’ assertions regarding the need to elicit patient
preferences. Numerous studies have demonstrated that the
preferred option will vary among well-informed patients,
even when one strategy is recommended by experts or
shown to be superior in analytic decision models. Therefore, high-quality decision making requires not only the
availability of appropriately tailored outcome data but
also an explicit assessment of how individual patients
value the outcomes associated with different treatment
alternatives.
Liana Fraenkel, MD, MPH
Terri Fried, MD
Author Affiliations: Department of Medicine, Yale University School of Medicine, New Haven, Connecticut; and
Clinical Epidemiology Research Center, VA Connecticut Healthcare System, West Haven.
Correspondence: Dr Fraenkel, Yale University/VA
Connecticut Healthcare System, 300 Cedar St, TAC
525, PO Box 208031, New Haven, CT 06520 (liana
[email protected]).
Time Spent on Clinical Documentation:
Is Technology a Help or a Hindrance?
W
e commend Oxentenko and colleagues1 on
their study demonstrating the excessive burden of clerical work reported by internal
medicine residents. The recognition of this important topic
and the effort to assess the educational value of these tasks
is critical to ensuring that future residency reforms focus not just on hours worked but also on the type of work
residents do.2 The authors identify faculty feedback as
one factor that may add value to clerical work. There may
be other factors that could make house staff activities more
or less useful. In fact, Dresselhaus and colleagues3 demonstrated that while house staff perceived direct patient
care activities to be more educational than indirect care,
the differences were smaller than one would anticipate
(ie, a score of 66 for “performing initial history and physical” vs as score of 39 for “documentation” on a scale of
0 to 100).
Given the increasing use of electronic health records
(EHRs) in teaching hospitals, we also wondered how
the time and value of certain clerical work related to
documentation has changed with health information
technology. Certainly, the advantages of such systems
include more efficient and safer order entry, easily
accessible clinical information, and the ability to facilitate documentation through decision support or documentation templates. As a result, one could theorize
that EHRs could streamline or eliminate many of the
low-value tasks that compose clerical work. Unfortunately, it is also possible that these technology solutions
could have the opposite effect of increasing the time
spent in clerical work and lowering the value of that
work.4 In our experience at the University of Chicago,
after implementing an EHR, residents often research a
new patient extensively on the EHR prior to the history
taking and physical examination, preferring to obtain
information via clerical work rather than direct patient
assessment. In addition, the well-described habits of
“cutting and pasting” notes or copying forward previous notes with minor daily updates are work-arounds
that may save time but provide little opportunity for
education and reflection about a patient’s course. 5
While it is hard to imagine that clerical work will not be
a significant part of residents’ duties in the future, it is
important to recognize that the method and content of
this clerical work is changing rapidly with the adoption
of EHRs. We hope future studies will examine the influence of EHRs on clerical work and propose strategies
for using health information technology to maximize
the educational value of necessary clerical tasks performed by internal medicine residents.
Jacob A. Doll, MD
Vineet Arora, MD, MAPP
Author Affiliations: Internal Medicine Residency, Department of Medicine, University of Chicago, Chicago,
Illinois.
Correspondence: Dr Arora, Department of Medicine,
University of Chicago, 5841 S Maryland Ave, MC 2007,
AMB W216, Chicago, IL 60637 ([email protected]
.uchicago.edu).
1. Oxentenko AS, West CP, Popkave C, Weinberger SE, Kolars JC. Time spent
on clinical documentation: a survey of internal medicine residents and program directors. Arch Intern Med. 2010;170(4):377-380.
2. Arora VM, Georgitis E, Siddique J, et al. Association of workload of on-call
medical interns with on-call sleep duration, shift duration, and participation
in educational activities. JAMA. 2008;300(10):1146-1153.
3. Dresselhaus TR, Luck J, Wright BC, Spragg RG, Lee ML, Bozzette SA. Analyzing the time and value of housestaff inpatient work. J Gen Intern Med. 1998;
13(8):534-540.
4. Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended consequences related to computerized provider order entry. J Am Med
Inform Assoc. 2006;13(5):547-556.
5. Hirschtick RE. A piece of my mind: copy-and-paste. JAMA. 2006;295(20):23352336.
In reply
We appreciate the insightful comments by Doll and Arora
relating to our article on how internal medicine residents
spend their time on inpatient services, in which we found
that 67.9% of residents reported spending in excess of 4 hours
daily on documentation, while only 38.9% reported spending this amount of time in direct patient contact.1 Although
feedback on documentation may increase the educational
value of this indirect patient care activity, it is infrequently
provided.
Measuring the educational value of trainee activities can
provide insight into where curricula can be enhanced. Doll
and Arora point out that the difference in educational value
between direct and indirect patient care was not as great as
one might expect in the study by Dresselhaus and colleagues,2 but we note that activities deemed “indirect patient care” (eg, documentation, order/obtain tests, discharge planning, initiating consultations) received the lowest
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scores compared with any direct patient care or educational activity, with the exception of reading literature regarding patients and discussion of patient issues, which
clearly have differing educational merit.
Doll and Arora raise the additional question of how the
time demands and merits of clinical documentation have
changed in the era of modern technology. Data on EHRs were
not part of the Internal Medicine In-Training Examination
Survey, but our experience has also been that electronic systems can both promote and impede efficiency and may create new medical errors that such systems were intended to
reduce. Doll and Arora suggest that the improved efficiency of the copy and paste function needs to be balanced
against the potential decrease in reflection on a patient’s clinical course. This also has implications for quality of care,
given a recent report that the copy and paste function leads
to mistakes in patient care.3
It is concerning that time spent on clerical documentation continues to consume more time for trainees than activities with higher educational value. Limiting tasks of low
educational value in medicine will have to be part of the solution; however, optimizing the educational merit of remaining tasks may better reflect the reality that clerical tasks
are unlikely to disappear altogether. We agree that consideration of the role of EHRs in striking the balance between
clerical necessity and optimal medical education will be of
critical importance.
Amy S. Oxentenko, MD
Colin P. West, MD, PhD
Joseph C. Kolars, MD
Author Affiliations: Divisions of Gastroenterology and
Hepatology (Dr Oxentenko) and General Internal Medicine (Dr West), Department of Internal Medicine, and
Division of Biomedical Statistics and Informatics, Department of Health Sciences Research (Dr West), Mayo
Clinic, Rochester, Minnesota; and Division of Gastroenterology, Department of Internal Medicine, University of
Michigan Medical School, Ann Arbor (Dr Kolars).
Correspondence: Dr Oxentenko, Department of Internal Medicine, Mayo Clinic, 200 First St SW, Rochester,
MN 55905 ([email protected]).
1. Oxentenko AS, West CP, Popkave C, Weinberger SE, Kolars JC. Time spent
on clinical documentation: a survey of internal medicine residents and program directors. Arch Intern Med. 2010;170(4):377-380.
2. Dresselhaus TR, Luck J, Wright BC, Spragg RG, Lee ML, Bozzette SA. Analyzing the time and value of housestaff inpatient work. J Gen Intern Med. 1998;
13(8):534-540.
3. O’Donnell HC, Kaushal R, Barron Y, Callahan MA, Adelman RD, Siegler EL.
Physicians’ attitudes towards copy and pasting in electronic note writing. J Gen
Intern Med. 2009;24(1):63-68.
Correction
Error in Text. In the Research Letter titled “Electronic
Medical Records and Upper Extremity Symptoms: Pain
With the Gain?” by Golomb et al, published in the April
12, 2010, issue of the Archives (2010;170[7]:655-657),
an error occurred in the final sentence of the “Results”
section on page 656. The sentence should read as follows: “The prediction of UEMSs by computer hours per
week and particularly by EMR-using clinic sessions
per week was preserved or strengthened in analysis adjusted for age, sex, and BMI (computer hours per week:
␤ = 0.601, SE = 0.194 [P = .003]; EMR-using clinic sessions per week: ␤=4.30, SE=1.56 [P=.008]) (Figure).”
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