Biomedical Informatics
2011 Year in Review
Notable publications and events in Informatics
since the 2010 AMIA Symposium
Daniel R. Masys, MD
Affiliate Professor
Biomedical and Health Informatics
University of Washington, Seattle
Content for this session is at:
http://faculty.washington.edu/dmasys/YearInReview
including citation lists and links
and this PowerPoint
Index to all Years in Review
http://faculty.washington.edu/dmasys/YearInReview
Design for this Session
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Modeled on American College of Physician
“Update” sessions
Emphasis on ‘what it is’ and ‘why it is
important’
Audience interaction for each category of
item discussed
Source of Content for Session
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Literature review of RCTs indexed by MeSH
term “Medical Informatics”, “Telemedicine” &
descendents or main MeSH term
“Bioinformatics”, and Entrez date between
November 2010 and October 2011 further
qualified by involvement of >100 providers or
patients
Poll of American College of Medical
Informatics fellows list
Thanks to:
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Russ Altman
David Bates
Bruce Buchanan
George Hripcsak
Ken Goodman
Christoph Lehmann
Harold Lehmann
Nancy Lorenzi
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Betsy Humphreys
Don Detmer
Blackford Middleton
Joyce Mitchell
Morris Collen
Bill Stead
Dean Sittig
Bill Tierney
Session components
Representative New Literature
 Notable Events – the ‘Top Five’ list
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New Literature Highlights:
Clinical Informatics
Clinical Decision Support
 Telemedicine
 The practice of informatics
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New Literature Highlights:
Bioinformatics and
Computational Biology
Human Health and Disease
 The practice of bioinformatics
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Clinical Decision
Support
51 new RCTs published
meeting search criteria
November 2010 – October 2011
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Magid DJ, et. al, A multimodal blood pressure control intervention in
3 healthcare systems. Am J Manag Care. 2011 Apr;17(4):e96-103
Source
 Institute for Health Research, Kaiser Permanente Colorado, Denver.
Aim
 To determine if a multimodal intervention composed of patient
education, home blood pressure (BP) monitoring, BP
measurement reporting to an interactive voice response (IVR)
phone system, and clinical pharmacist follow-up improves BP
control compared with usual care.
Methods
 RCT conducted at 3 healthcare systems in Denver, Colorado,
including a large health maintenance organization, a Veterans Affairs
medical center, and a county hospital.
 Prospective study with patient enrollment, medication consultation
and adjustment, remote BP monitoring, and follow-up at 6 months.
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Magid DJ, et. al, A multimodal blood pressure control intervention in 3
healthcare systems. Am J Manag Care. 2011 Apr;17(4):e96-103..
Methods, cont’d
 At each site, patients with uncontrolled BP were randomized to the
multimodal intervention vs usual care for 6 months, with the primary
end point of BP reduction.
Results
 338 patients randomized, 283 (84%) completed the study, including
138 intervention patients and 145 usual care patients.
 BP was higher in the intervention group vs the usual care group
(150.5/89.4 vs 143.8/85.3 mm Hg).
 At 6 months, BPs were similar in the intervention group vs the usual
care group (137.4 vs 136.7 mm Hg, P = .85 for systolic; 82.9 vs 81.1
mm Hg, P = .14 for diastolic).
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Magid DJ, et. al, A multimodal blood pressure control intervention in 3
healthcare systems. Am J Manag Care. 2011 Apr;17(4):e96-103.
Results, cont’d
 BP reductions were greater in the intervention group vs the usual care
group (−13.1 vs −7.1 mm Hg, P = .006 for systolic; −6.5 vs −4.2 mm
Hg, P = .07 for diastolic).
 Adherence to medications was similar between the 2 groups, but
intervention patients had a greater increase in medication regimen
intensity.
Conclusions
 A multimodal intervention of patient education, home BP monitoring,
BP measurement reporting to an IVR system, and clinical pharmacist
follow-up achieved greater reductions in BP compared with usual
care.
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Magid DJ, et. al, A multimodal blood pressure control intervention in 3
healthcare systems. Am J Manag Care. 2011 Apr;17(4):e96-103.
Importance
 Hybrid decision support systems such as automated phone response
systems, combined with self-generated physiologic monitoring
observations, and a human effector arm (clinical pharmacist followup), can provide effective sociotechnical approaches to decision
support.
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Bosworth HB, et. al, Home blood pressure management and improved
blood pressure control: results from a randomized controlled trial. Arch
Intern Med. 2011 Jul 11;171(13):1173-80.
Source
 Health Services Research and Development, Durham Veterans
Affairs Medical Center, Durham, NC
Aim
 To determine which of 3 interventions was most effective in improving
blood pressure (BP) control
Methods
 4-arm randomized trial with 18-month follow-up at the primary care
clinics at a Veterans Affairs Medical Center.
 Pts randomized to either usual care or 1 of 3 telephone-based
intervention groups:
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(1) nurse-administered behavioral management,
(2) nurse- and physician-administered medication management, or
(3) a combination of both.
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Bosworth HB, et. al, Home blood pressure management and improved
blood pressure control: results from a randomized controlled trial. Arch
Intern Med. 2011 Jul 11;171(13):1173-80.
Methods, cont’d
 Intervention telephone calls were triggered based on home BP
values transmitted via telemonitoring devices.
 Behavioral management involved promotion of health behaviors.
Medication management involved adjustment of medications by a
study physician and nurse based on hypertension treatment
guidelines.
 Primary outcome was change in BP control measured at 6-month
intervals over 18 months
Results
 593 individuals enrolled; 48% were African American.
 Both behavioral management and medication management alone
showed significant improvements at 12 months-12.8% (95%
confidence interval [CI], 1.6%-24.1%) and 12.5% (95% CI, 1.3%23.6%), respectively-but not at 18 months.
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Bosworth HB, et. al, Home blood pressure management and improved
blood pressure control: results from a randomized controlled trial. Arch
Intern Med. 2011 Jul 11;171(13):1173-80.
Results, cont’d
 In subgroup analyses, among those with poor baseline BP control,
systolic BP decreased in the combined intervention group by 14.8 mm
Hg (95% CI, -21.8 to -7.8 mm Hg) at 12 months and 8.0 mm Hg (95%
CI, -15.5 to -0.5 mm Hg) at 18 months, relative to usual care.
Conclusions
 Overall intervention effects were moderate, but among individuals with
poor BP control at baseline, the effects were larger.
 Study indicates the importance of identifying individuals most likely to
benefit from potentially resource intensive programs
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Bosworth HB, et. al, Home blood pressure management and improved
blood pressure control: results from a randomized controlled trial. Arch
Intern Med. 2011 Jul 11;171(13):1173-80.
Importance
 Example of automated physiologic home monitoring alerting clinic
personnel to intervene in a chronic condition with an effector
mechanism involving interpersonal communications.
 Similar interventions will be increasingly enabled by wireless
communications and low cost/high reliability solid state monitors
.
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Schnipper JL, et. al, Effects of documentation-based decision support
on chronic disease management. A m J Manag Care. 2010 Dec;16(12
Suppl HIT):SP72-81.
Source
 Division of General Medicine and Primary Care, Brigham and
Women's Hospital, Boston, MA
Aim
 To evaluate whether a new documentation-based clinical decision
support system (CDSS) is effective in addressing deficiencies in the
care of patients with coronary artery disease (CAD) and diabetes
mellitus (DM).
Methods
 Controlled trial randomized by physician.
 Primary care physicians (PCPs) in 10 ambulatory practices assigned
to usual care or a CAD/DM Smart Form for 9 months.
 Primary outcome was the proportion of deficiencies in care that were
addressed within 30 days after a patient visit
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Schnipper JL, et. al, Effects of documentation-based decision support
on chronic disease management. A m J Manag Care. 2010 Dec;16(12
Suppl HIT):SP72-81.
Results
 Smart Form was used for 5.6% of eligible patients.
 In the intention-to-treat analysis, patients of intervention PCPs had a
greater proportion of deficiencies addressed within 30 days of a visit
compared with controls (11.4% vs 10.1%, adjusted and clustered odds
ratio =1.14; 95% confidence interval, 1.02-1.28; P = .02).
 Differences were more pronounced in the "on-treatment" analysis:
17.0% of deficiencies were addressed after visits in which the Smart
Form was used compared with 10.6% of deficiencies after visits in
which it was not used (P <.001).
 Measures that improved included documentation of smoking status
and prescription of antiplatelet agents when appropriate..
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Schnipper JL, et. al, Effects of documentation-based decision support
on chronic disease management. A m J Manag Care. 2010 Dec;16(12
Suppl HIT):SP72-81.
Conclusions
 Overall use of the CAD/DM Smart Form was low, and improvements
in management were modest.
 When used, documentation-based decision support shows promise.
Importance
 Systems have to be used in order to have any effects on care
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Fitzgerald M, et. al, Trauma resuscitation errors and computerassisted decision support. Arch Surg. 2011 Feb;146(2):218-25.
Source
 The Alfred Hospital, Melbourne, Victoria, Australia.
Aim
 To determine whether computer-aided decision support during the first
30 minutes of trauma resuscitation reduces management .
Methods
 Prospective, open, randomized, controlled interventional study that
evaluated the effect of real-time, computer-prompted, evidence-based
decision and action algorithms on error occurrence during initial
resuscitation between January 24, 2006, and February 25, 2008.
 Enrolled severely injured adults at a level I trauma center
Results
 1171 patients were recruited into 3 groups: 300 into a baseline control
group, 436 into a concurrent control group, and 435 into the study
group.
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Fitzgerald M, et. al, Trauma resuscitation errors and computerassisted decision support. Arch Surg. 2011 Feb;146(2):218-25.
Results, cont’d
 There was a reduction in error rate per patient from the baseline
control group to the study group (2.53 to 2.13, P = .004) and from the
control group to the study group (2.30 to 2.13, P = .04).
 A critical decision was required every 72 seconds, and error-free
resuscitations were increased from 16.0% to 21.8% (P = .049) during
the first 30 minutes of resuscitation.
 Morbidity from shock management (P = .03), blood use (P < .001), and
aspiration pneumonia (P = .046) were decreased..
Conclusions
 Computer-aided, real-time decision support resulted in improved
protocol compliance and reduced errors and morbidity.
Clinical Decision Support for Providers:
cardiovascular diseases
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Reference
 Fitzgerald M, et. al, Trauma resuscitation errors and computerassisted decision support. Arch Surg. 2011 Feb;146(2):218-25.
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Importance
 Effective real time decision support can be designed for timepressured, high intensity clinical interactions such as trauma
resuscitation.
 Substantial additional opportunity for improvement.
 Stay healthy, my friends…
Clinical Decision Support for Providers:
cancer detection and treatment
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Reference
 Imperiale TF et. al, Provider acceptance, safety, and effectiveness of
a computer-based decision tool for colonoscopy preparation. Int J Med
Inform. 2011 Oct;80(10):726-33.
Source
 Indianapolis VA Medical Center, Indiana University Medical Center
and Regenstrief Institute, Indianapolis, IN.
Aims
 To assess provider acceptance of recommendations by a decision
tool that scans the electronic medical record and determines whether
sodium phosphate may be taken.
 To determine decision tool effects on a composite outcome of
colonoscopies canceled, rescheduled, aborted, or repeated sooner
than recommended due to preparation (prep) quality; prep quality;
colonoscopy duration; and patient satisfaction with and tolerance of
the preparation..
Methods
 Four alternating four-week periods to compare the decision tool with
usual care for outpatient colonoscopy.
Clinical Decision Support for Providers:
cancer detection and treatment
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Reference
 Imperiale TF et. al, Provider acceptance, safety, and effectiveness of
a computer-based decision tool for colonoscopy preparation. Int J Med
Inform. 2011 Oct;80(10):726-33.
Methods, cont’d
 All decision tool decisions were reviewed in real-time by
gastroenterology nurses and/or physicians.
 Patients completed a survey about the prep process.
 Endoscopists blindly rated prep quality.
 Colonoscopy duration and findings were recorded.
Results
 Of 354 persons in the decision tool group, 4 prep decisions were
overridden because of patient preference or prior prep failure, but
none for medical reasons.
 Sodium phosphate was used more frequently in the decision tool
group (73% vs. 41%; P<0.01).
Clinical Decision Support for Providers:
cancer detection and treatment
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Reference
 Imperiale TF et. al, Provider acceptance, safety, and effectiveness of
a computer-based decision tool for colonoscopy preparation. Int J Med
Inform. 2011 Oct;80(10):726-33.
Results, cont’d
 No difference between the decision tool and usual care groups in the
composite outcome (26% vs. 30%, respectively; P=0.29), acceptable
prep quality (62% vs. 56%; P=0.22), colonoscopy duration (28 vs.
30min; P=0.17), patient satisfaction (P=0.38), or preparation tolerance
(P=0.37)
Conclusions
 An electronic medical record-based decision tool can safely and
effectively tailor the prep for colonoscopy and may improve
colonoscopy efficiency and patient satisfaction
Clinical Decision Support for Providers:
cancer detection and treatment
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Reference
 Imperiale TF et. al, Provider acceptance, safety, and effectiveness of
a computer-based decision tool for colonoscopy preparation. Int J Med
Inform. 2011 Oct;80(10):726-33.
Importance
 CDSS systems can be effectively applied to low intensity settings
where elective choices about outpatient procedures may have large
multiplier effects based on the frequency and cost of the procedures
planned.
Clinical Decision Support for Providers:
cancer detection and treatment
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Reference
 Cleeland CS, et. al, Automated symptom alerts reduce postoperative
symptom severity after cancer surgery: a randomized controlled
clinical trial. J Clin Oncol. 2011 Mar 10;29(8):994-1000.
Source
 MD Anderson Cancer Center, Houston, TX.
Aim
 To determine whether at-home symptom monitoring plus feedback to
clinicians about severe symptoms contributes to more effective
postoperative symptom control after cancer-related thoracotomy.
Methods
 A two arm RCT for patients receiving thoracotomy for lung cancer or
lung metastasis.
 After hospital discharge, patients rated symptoms twice weekly for
4 weeks via automated telephone calls.
 For intervention group patients, an e-mail alert was forwarded to
the patient's clinical team for response if any of a subset of
symptoms (pain, disturbed sleep, distress, shortness of breath, or
constipation) reached a predetermined severity threshold.
Clinical Decision Support for Providers:
cancer detection and treatment
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Reference
 Cleeland CS, et. al, Automated symptom alerts reduce postoperative
symptom severity after cancer surgery: a randomized controlled
clinical trial. J Clin Oncol. 2011 Mar 10;29(8):994-1000.
Methods, cont’d
 No alerts were generated for controls.
 Group differences in symptom threshold events were examined by
generalized estimating equation modeling.
Results
 100 patients enrolled; 79 patients completed the study.
 Intervention group experienced greater reduction in symptom
threshold events than did controls (19% v 8%, respectively) and a
more rapid decline in symptom threshold events.
 Difference in average reduction in symptom interference between
groups was -0.36 (SE, 0.078; P = .02).
 Clinicians responded to 84% of e-mail alerts.
Clinical Decision Support for Providers:
cancer detection and treatment
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Reference
 Cleeland CS, et. al, Automated symptom alerts reduce postoperative
symptom severity after cancer surgery: a randomized controlled
clinical trial. J Clin Oncol. 2011 Mar 10;29(8):994-1000..
Results, cont’d
 Both groups reported equally high satisfaction with the automated
system and with postoperative symptom control.
Conclusions
 Frequent symptom monitoring with alerts to clinicians when symptoms
became moderate or severe reduced symptom severity during the 4
weeks after thoracic surgery.
 Methods of automated symptom monitoring and triage may improve
symptom control after major cancer surgery.
Importance
 Example of systems approach to acquiring symptom data, automated
filtering and clinician alerting
Clinical Decision Support for Providers:
cancer detection and treatment
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Reference
 Berry DL, et. al, Enhancing patient-provider communication with the
electronic self-report assessment for cancer: a randomized trial. J Clin
Oncol. 2011 Mar 10;29(8):1029-35. Epub 2011 Jan 31.
Source
 Harvard Medical School, Boston, MA.
Aim
 T determine the effect of the Electronic Self-Report AssessmentCancer (ESRA-C) on the likelihood of Symptoms and Quality of Life
Issues (SQLIs) being discussed between clinicians and patients with
cancer in ambulatory clinic visits.
 Secondary objectives included comparison of visit duration between
groups and usefulness of the ESRA-C as reported by clinicians..
Methods
 RCT in Pts with various cancer diagnoses and stages at two
institutions of a comprehensive cancer center.
 Patient-reported SQLIs were automatically displayed on a graphical
summary and provided to the clinical team before an on-treatment
visit.
 In control group, no summary was provided.
Clinical Decision Support for Providers:
cancer detection and treatment
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Reference
 Berry DL, et. al, Enhancing patient-provider communication with the
electronic self-report assessment for cancer: a randomized trial. J Clin
Oncol. 2011 Mar 10;29(8):1029-35. Epub 2011 Jan 31.
Methods, cont’d
 SQLIs were scored for level of severity or distress.
 One on-treatment clinic visit was audio recorded for each participant
and then scored for discussion of each SQLI.
 Authors hypothesized that problematic SQLIs would be discussed
more often when the intervention was delivered to the clinicians.
Results
 The likelihood of SQLIs being discussed differed by randomized group
and depended on whether an SQLI was first reported as problematic
(P = .032).
 Clinic visits were similar with regard to duration between groups, and
clinicians reported the summary as useful
Clinical Decision Support for Providers:
cancer detection and treatment
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Reference
 Berry DL, et. al, Enhancing patient-provider communication with the
electronic self-report assessment for cancer: a randomized trial. J Clin
Oncol. 2011 Mar 10;29(8):1029-35. Epub 2011 Jan 31.
Conclusion
 The ESRA-C is the first electronic self-report application to increase
discussion of SQLIs in a US randomized clinical trial.
Importance
 Contributes to the emerging trend of capturing data from patients,
formatting it and presenting it to clinicians to encourage clinical
management behaviors that represent best practice.
Clinical Decision Support for Providers:
diabetes
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Reference
 Cebul RD et. al, Electronic health records and quality of diabetes care.
N Engl J Med. 2011 Sep 1;365(9):825-33.
Source
 Department of Medicine, Case Western Reserve University at
MetroHealth Medical Center, Cleveland, OH.
Aim
 To compare achievement of and improvement in quality standards for
diabetes at practices using EHRs with CDSS with those at practices
using paper records.
Methods
 All practices, including many safety-net primary care practices,
belonged to a regional quality collaborative and publicly reported
performance.
 Authors used generalized estimating equations to calculate the
percentage-point difference between EHR-based and paper-based
practices with respect to achievement of composite standards for
diabetes care (including four component standards) and outcomes
(five standards), after adjusting for covariates and accounting for
clustering.
Clinical Decision Support for Providers:
diabetes
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Reference
 Cebul RD et. al, Electronic health records and quality of diabetes care.
N Engl J Med. 2011 Sep 1;365(9):825-33
Methods, cont’d
 Covariates included insurance type (Medicare, commercial, Medicaid,
or uninsured), race or ethnic group (white, black, Hispanic, or other),
age, sex, estimated household income, and level of education.
 Analyses were conducted separately for the overall sample and for
safety-net practices.
Results
 From July 2009 through June 2010, data were reported for 27,207
adults with diabetes seen at 46 practices; safety-net practices
accounted for 38% of patients.
 After adjustment for covariates, achievement of composite standards
for diabetes care was 35.1 percentage points higher at EHR sites than
at paper-based sites (P<0.001), and achievement of composite
standards for outcomes was 15.2 percentage points higher (P=0.005).
Clinical Decision Support for Providers:
diabetes
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Reference
 Cebul RD et. al, Electronic health records and quality of diabetes care.
N Engl J Med. 2011 Sep 1;365(9):825-33
Results, cont’d
 EHR sites were associated with higher achievement on eight of nine
component standards.
 Such sites were also associated with greater improvement in care (a
difference of 10.2 percentage points in annual improvement, P<0.001)
and outcomes (a difference of 4.1 percentage points in annual
improvement, P=0.02).
 Across all insurance types, EHR sites were associated with
significantly higher achievement of care and outcome standards and
greater improvement in diabetes care.
 Results confined to safety-net practices were similar.
Clinical Decision Support for Providers:
diabetes
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Reference
 Cebul RD et. al, Electronic health records and quality of diabetes care.
N Engl J Med. 2011 Sep 1;365(9):825-33
Conclusions
 Findings support the premise that federal policies encouraging the
meaningful use of EHRs may improve the quality of care across
insurance types.
Importance
 Regional-scale assessment for a common chronic condition reports a
beneficial effect of EHRs with CDSS.
Clinical Decision Support for Providers:
diabetes
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Reference
 O'Connor PJ, et. al, Impact of electronic health record clinical decision
support on diabetes care: a randomized trial. Ann Fam Med. 2011
Jan-Feb;9(1):12-21.
Source
 Health Partners Medical Group, Minneapolis, MN.
Aim
 To assess the impact of an electronic health record-based diabetes
clinical decision support system on control of hemoglobin A(1c)
(glycated hemoglobin), blood pressure, and low-density lipoprotein
(LDL) cholesterol levels in adults with diabetes.
Methods
 Clinic-randomized trial conducted from October 2006 to May 2007 in
Minnesota.
 Patients were randomized either to receive or not to receive an
electronic health record (EHR)-based clinical decision support system
designed to improve care for those patients whose hemoglobin A(1c),
blood pressure, or LDL cholesterol levels were higher than goal at any
office visit.
Clinical Decision Support for Providers:
diabetes
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Reference
 O'Connor PJ, et. al, Impact of electronic health record clinical decision
support on diabetes care: a randomized trial. Ann Fam Med. 2011
Jan-Feb;9(1):12-21.
Methods, cont’d
 Analysis used general and generalized linear mixed models with
repeated time measurements to accommodate the nested data
structure.
Results
 11 clinics with 41 consenting primary care physicians and the
physicians' 2,556 patients with diabetes enrolled.
 The intervention group physicians used the EHR-based decision
support system at 62.6% of all office visits made by adults with
diabetes.
 Intervention group diabetes patients had significantly better
hemoglobin A(1c) (intervention effect -0.26%; 95% confidence interval,
-0.06% to -0.47%; P=.01), and better maintenance of systolic blood
pressure control (80.2% vs 75.1%, P=.03).
Clinical Decision Support for Providers:
diabetes
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Reference
 O'Connor PJ, et. al, Impact of electronic health record clinical decision
support on diabetes care: a randomized trial. Ann Fam Med. 2011
Jan-Feb;9(1):12-21.
Methods, cont’d
 Analysis used general and generalized linear mixed models with
repeated time measurements to accommodate the nested data
structure.
Results, cont’d
 Intervention group had borderline better maintenance of diastolic blood
pressure control (85.6% vs 81.7%, P =.07), but not improved lowdensity lipoprotein cholesterol levels (P = .62) than patients of
physicians randomized to the control arm of the study.
 Among intervention group physicians, 94% were satisfied or very
satisfied with the intervention, and moderate use of the support system
persisted for more than 1 year after feedback and incentives to
encourage its use were discontinued.
Clinical Decision Support for Providers:
diabetes
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Reference
 O'Connor PJ, et. al, Impact of electronic health record clinical decision
support on diabetes care: a randomized trial. Ann Fam Med. 2011
Jan-Feb;9(1):12-21.
Conclusions
 EHR-based diabetes clinical decision support significantly improved
glucose control and some aspects of blood pressure control in adults
with type 2 diabetes.
Importance
 Adds to literature on utility of outpatient CDSS systems for high
prevalence chronic conditions.
 CDSS studies that use objective physiologic data (BP, HbA1C) have
an advantage in presentation and interpretation of results
Clinical Decision Support for Providers:
infectious diseases
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Reference
 Humiston SG, et. al, Increasing inner-city adult influenza vaccination
rates: a randomized controlled trial. Public Health Rep. 2011 JulAug;126 Suppl 2:39-47.
Source
 Department of Pediatrics, University of Rochester School of Medicine
and Dentistry, Rochester, NY.
Aim
 To evaluate the effect of the practice-based intervention on influenza
immunization rates and disparities in vaccination rates by
race/ethnicity and insurance status.
Methods
 RCTl during 2003-2004 testing patient tracking/recall/outreach and
provider prompts on improving influenza immunization rates.
 Patients aged > or = 65 years in six large inner-city primary care
practices were randomly allocated to study or control group.
 Influenza immunization coverage was measured prior to enrollment
and on the end date..
Clinical Decision Support for Providers:
infectious diseases
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Reference
 Humiston SG, et. al, Increasing inner-city adult influenza vaccination
rates: a randomized controlled trial. Public Health Rep. 2011 JulAug;126 Suppl 2:39-47.
Results
 At study end, immunization rates were greater for the intervention
group than for the control group (64% vs. 22%, p < 0.0001).
 When controlling for other factors, the intervention group was more
than six times as likely to receive influenza vaccine.
 The intervention was effective across gender, race/ ethnicity, age, and
insurance subgroups.
 Among the intervention group, 3.5% of African Americans and 3.2% of
white people refused influenza immunization..
Clinical Decision Support for Providers:
infectious diseases



Reference
 Humiston SG, et. al, Increasing inner-city adult influenza vaccination
rates: a randomized controlled trial. Public Health Rep. 2011 JulAug;126 Suppl 2:39-47.
Conclusions
 Patient tracking/recall/outreach and provider prompts were intensive
but successful approaches to increasing seasonal influenza
immunization rates among inner-city seniors
Importance
 Few CDSS studies show 300% improvement over baseline (and 2/3
of all events in compliance) with preventive medicine best practices
Clinical Decision Support for Providers:
infectious diseases




Reference
 Szilagyi PG, et. al, Effectiveness of a citywide patient immunization
navigator program on improving adolescent immunizations and
preventive care visit rates. Arch Pediatr Adolesc Med. 2011
Jun;165(6):547-53.
Source
 Department of Pediatrics, University of Rochester School of Medicine
and Dentistry, Rochester, NY.
Aim
 To assess the impact of a tiered patient immunization navigator
intervention (immunization tracking, reminder/recall, and outreach) on
improving immunization and preventive care visit rates in urban
adolescents.
Methods
 Same informatics infrastructure as that used for inner city adults,
applied to urban adolescents.
 Immunization ‘navigators’ at each practice implemented a tiered
protocol: immunization tracking, telephone or mail reminder/recall, and
home visits if participants remained unimmunized or behind on
preventive care visits
Clinical Decision Support for Providers:
infectious diseases


Reference
 Szilagyi PG, et. al, Effectiveness of a citywide patient immunization
navigator program on improving adolescent immunizations and
preventive care visit rates. Arch Pediatr Adolesc Med. 2011
Jun;165(6):547-53.
Results
 Intervention and control groups were similar at baseline for
demographics (mean age, 13.5 years; 63% black, 14% white, and
23% Hispanic adolescents; and 74% receiving Medicaid)
 Immunization rates at the end of the study were 44.7% for the
intervention group and 32.4% for the control group (adjusted risk ratio,
1.4; 95% confidence interval, 1.3-1.5)
 Preventive care visit rates were 68.0% for the intervention group and
55.2% for the control group (1.2; 1.2-1.3).
 Findings were similar across practices, sexes, ages, and insurance
providers.
Clinical Decision Support for Providers:
infectious diseases


Reference
 Szilagyi PG, et. al, Effectiveness of a citywide patient immunization
navigator program on improving adolescent immunizations and
preventive care visit rates. Arch Pediatr Adolesc Med. 2011
Jun;165(6):547-53.
Results, cont’d
 The intervention cost was $3.81 per adolescent per month
 The cost per additional adolescent fully vaccinated was $465, and the
cost per additional adolescent receiving a preventive care visit was
$417.
Clinical Decision Support for Providers:
infectious diseases



Reference
 Szilagyi PG, et. al, Effectiveness of a citywide patient immunization
navigator program on improving adolescent immunizations and
preventive care visit rates. Arch Pediatr Adolesc Med. 2011
Jun;165(6):547-53.
Conclusions
 A tiered tracking, reminder/recall, and outreach intervention improved
immunization and preventive care visit rates in urban adolescents.
Importance
 Study presents cost-effectiveness data which relatively few CDSS
studies do (but probably all should in the current healthcare
environment)
Clinical Decision Support for Providers:
infectious diseases




Reference
 Were MC, et. Al (including Tierney), Evaluation of computer-generated
reminders to improve CD4 laboratory monitoring in sub-Saharan
Africa: a prospective comparative study. J Am Med Inform Assoc.
2011 Mar-Apr;18(2):150-5. Epub 2011 Jan 20.
Source
 Indiana University School of Medicine, Indianapolis, IN.
Aim
 To test the hypothesis that clinical summaries with computergenerated reminders could improve clinicians' compliance with CD4
testing guidelines in the resource-limited setting of sub-Saharan
Africa..
Methods
 A prospective comparative study of two randomly selected outpatient
adult HIV clinics in western Kenya.
 Printed summaries with reminders for overdue CD4 tests were made
available to clinicians in the intervention clinic but not in the control
clinic.
Clinical Decision Support for Providers:
infectious diseases



Reference
 Were MC, et. al, Evaluation of computer-generated reminders to
improve CD4 laboratory monitoring in sub-Saharan Africa: a
prospective comparative study. J Am Med Inform Assoc. 2011 MarApr;18(2):150-5. Epub 2011 Jan 20.
Methods, cont’d
 Changes in order rates for overdue CD4 tests were compared
between and within the two clinics.
Results
 The computerized reminder system identified 717 encounters (21%)
with overdue CD4 tests.
 Analysis by study assignment (regardless of summaries being printed
or not) revealed that with computer-generated reminders, CD4 order
rates were significantly higher in the intervention clinic compared to the
control clinic (53% vs 38%, OR = 1.80, CI 1.34 to 2.42, p < 0.0001).
Clinical Decision Support for Providers:
infectious diseases



Reference
 Were MC, et. al, Evaluation of computer-generated reminders to
improve CD4 laboratory monitoring in sub-Saharan Africa: a
prospective comparative study. J Am Med Inform Assoc. 2011 MarApr;18(2):150-5. Epub 2011 Jan 20.
Methods, cont’d
 Changes in order rates for overdue CD4 tests were compared
between and within the two clinics.
Results
 When comparison was restricted to encounters where summaries with
reminders were printed, order rates in intervention clinic were even
higher (63%).
 The intervention clinic increased CD4 ordering from 42% before
reminders to 63% with reminders (50% increase, OR = 2.32, CI 1.67 to
3.22, p < 0.0001), compared to control clinic with only 8% increase
from prestudy baseline (CI 0.83 to 1.46, p = 0.51).
Clinical Decision Support for Providers:
infectious diseases



Reference
 Were MC, et. al, Evaluation of computer-generated reminders to
improve CD4 laboratory monitoring in sub-Saharan Africa: a
prospective comparative study. J Am Med Inform Assoc. 2011 MarApr;18(2):150-5. Epub 2011 Jan 20.
Conclusions
 Clinical summaries with computer-generated reminders significantly
improved clinician compliance with CD4 testing guidelines in the
resource-limited setting of sub-Saharan Africa.
 This technology can have broad applicability to improve quality of HIV
care in these settings.
Importance
 CDSS’s are not just for high tech healthcare settings
Clinical Decision Support for Providers:
medication management




Reference
 Lapane KL, et. al, Effect of a pharmacist-led multicomponent
intervention focusing on the medication monitoring phase to prevent
potential adverse drug events in nursing homes. J Am Geriatr Soc.
2011 Jul;59(7):1238-45.
Source
 Department of Epidemiology and Community Health, Virginia
Commonwealth University, Richmond, VA.
Aim
 To determine the extent to which the use of a clinical informatics tool
that implements prospective monitoring plans reduces the incidence
of potential delirium, falls, hospitalizations potentially due to adverse
drug events, and mortality.
Methods
 Randomized cluster trial in twenty-five nursing homes serviced by two
long-term care pharmacies
 Pharmacy automatically generated Geriatric Risk Assessment
MedGuide (GRAM) reports and automated monitoring plans for falls
and delirium within 24 hours of admission or as part of the normal time
frame of federally mandated drug regimen review.
Clinical Decision Support for Providers:
medication management



Reference
 Lapane KL, et. al, Effect of a pharmacist-led multicomponent
intervention focusing on the medication monitoring phase to prevent
potential adverse drug events in nursing homes. J Am Geriatr Soc.
2011 Jul;59(7):1238-45.
Methods, cont’d
 Measured incidence of potential delirium, falls, hospitalizations
potentially due to adverse drug events, and mortality.
Results
 Study included residents living in nursing homes during 2003 (1,711 in
12 intervention; 1,491 in 13 usual care) and 2004 (1,769 in 12
intervention; 1,552 in 13 usual care).
 GRAM triggered monitoring plans for 491 residents.
 Newly admitted residents in the intervention homes experienced a
lower rate of potential delirium onset than those in usual care homes
(adjusted hazard ratio (HR)=0.42, overall hospitalization (adjusted
HR=0.89), and mortality (adjusted HR=0.88).
Clinical Decision Support for Providers:
medication management




Reference
 Lapane KL, et. al, Effect of a pharmacist-led multicomponent
intervention focusing on the medication monitoring phase to prevent
potential adverse drug events in nursing homes. J Am Geriatr Soc.
2011 Jul;59(7):1238-45.
Results, cont’d
 In longer stay residents, the effects of the intervention were
attenuated, and all estimates included unity.
Conclusions
 Using CDSS technology in long-term care pharmacies may reduce
adverse effects associated with medication use.
Importance
 Systems approaches to care help in nursing homes also
Clinical Decision Support for Providers:
medication management




Reference
 Bhardwaja B, et. al, Improving prescribing safety in patients with renal
insufficiency in the ambulatory setting: the Drug Renal Alert Pharmacy
(DRAP) program. Pharmacotherapy. 2011 Apr;31(4):346-56..
Source
 Pharmacy Department, Kaiser Permanente Colorado, Denver, CO.
Aim
 To determine whether a computerized Drug Renal Alert Pharmacy
(DRAP) program could decrease the rate of medication errors in drug
selection or dosing for 15 target drugs in patients with renal
insufficiency.
Methods
 RCT involving a computerized too to alert pharmacists at the time of
dispensing to possible errors in target drug selection and dosing for
patients with renal insufficiency.
 15 target drugs identified based on frequency of use and risk of
serious adverse events
Clinical Decision Support for Providers:
medication management



Reference
 Bhardwaja B, et. al, Improving prescribing safety in patients with renal
insufficiency in the ambulatory setting: the Drug Renal Alert Pharmacy
(DRAP) program. Pharmacotherapy. 2011 Apr;31(4):346-56.
Methods, cont’d
 Primary outcome was the proportion of medication errors, defined as
target drugs that should be avoided or were dosed inappropriately, in
the intervention and usual care groups. .
Results
 Of eligibile 32,917 patients, 6125 patients (3025 in the intervention
group and 3100 in the usual care group) were prescribed at least one
target drug and were included in the analysis.
 No significant differences in baseline characteristics between groups.
 Over the 15-month intervention period, the proportion of medication
errors was significantly lower in the intervention group than the usual
care group (33% vs 49%, p<0.001).
Clinical Decision Support for Providers:
medication management




Reference
 Bhardwaja B, et. al, Improving prescribing safety in patients with renal
insufficiency in the ambulatory setting: the Drug Renal Alert Pharmacy
(DRAP) program. Pharmacotherapy. 2011 Apr;31(4):346-56.
Results, cont’d
 After the study period, when the intervention was expanded to both
groups, a 20% reduction in errors was sustained in the combined
groups over the subsequent 7 months.
Conclusions
 The DRAP program was successful in reducing medication errors for
patients with renal insufficiency in an ambulatory setting and was
demonstrated to have sustainability after study completion.
Importance
 Good news: medication errors dropped from roughly half of patients to
a third of patients
 Bad news: a third of patients still had medication errors
Clinical Decision Support for Providers:
nursing care




Reference
 Dykes PC, et. al, Fall prevention in acute care hospitals: a randomized
trial. JAMA. 2010 Nov 3;304(17):1912-8...
Source
 Brigham and Women's Hospital/Partners HealthCare System, Harvard
Medical School, Boston, MA.
Aim
 To investigate whether a fall prevention tool kit (FPTK) using health
information technology (HIT) decreases patient falls in hospitals.
Methods
 Cluster randomized study comparing patient fall rates in 4 urban US
hospitals in units that received usual care (4 units and 5104 patients)
or the intervention (4 units and 5160 patients)
 The FPTK integrated existing communication and workflow patterns
into the HIT application.
 Based on a valid fall risk assessment scale completed by a nurse, the
FPTK software tailored fall prevention interventions to address
patients' specific determinants of fall risk.
 FPTK produced bed posters composed of brief text with an
accompanying icon, patient education handouts, and plans of care
Clinical Decision Support for Providers:
nursing care



Reference
 Dykes PC, et. al, Fall prevention in acute care hospitals: a randomized
trial. JAMA. 2010 Nov 3;304(17):1912-8.
Methods, cont’d
 The primary outcome was patient falls per 1000 patient-days adjusted
for site and patient care unit. A secondary outcome was fall-related
injuries .
Results
 During the 6-month intervention period, the number of patients with
falls differed between control (n = 87) and intervention (n = 67) units
(P=.02).
 Site-adjusted fall rates were significantly higher in control units (4.18
[95% confidence interval {CI}, 3.45-5.06] per 1000 patient-days) than
in intervention units (3.15 [95% CI, 2.54-3.90] per 1000 patient-days; P
= .04). .
Clinical Decision Support for Providers:
nursing care




Reference
 Dykes PC, et. al, Fall prevention in acute care hospitals: a randomized
trial. JAMA. 2010 Nov 3;304(17):1912-8.
Results, cont’d
 FPTK was found to be particularly effective with patients aged 65
years or older (adjusted rate difference, 2.08 [95% CI, 0.61-3.56] per
1000 patient-days; P = .003).
 No significant effect was noted in fall-related injuries.
Conclusions
 The use of a fall prevention tool kit in hospital units compared with
usual care significantly reduced rate of falls.
Importance
 Hybrid outputs from CDSS applications (e.g., bed posters, Pt
handouts, care plans) can be effective for common inpatient risks
Clinical Decision Support for Patients:
cancer detection (4 RCTs)

References
 Miller DP Jr, et. al, Effectiveness of a web-based colorectal cancer
screening patient decision aid: a randomized controlled trial in a
mixed-literacy population. Am J Prev Med. 2011 Jun;40(6):608-15.
[Wake Forest, Salem NC]

Hoffman RM, et. al, A system-based intervention to improve
colorectal cancer screening uptake. Am J Manag Care. 2011
Jan;17(1):49-55. [New Mexico VA]

Leffler DA, et. al, An alerting system improves adherence to
follow-up recommendations from colonoscopy examinations.
Gastroenterology. 2011 Apr;140(4):1166-1173.e1-3. [Beth Israel
Deaconess, Boston]

Cameron KA, et. al, Patient outreach to promote colorectal cancer
screening among patients with an expired order for colonoscopy:
a randomized controlled trial. Arch Intern Med. 2011 Apr
11;171(7):642-6. [Northwestern U., Chicago]..
Clinical Decision Support for Patients:
cancer detection (4 RCTs)


Interventions
 Wake Forest: web-based CRC educational tool viewed just before
appointment.
 New Mexico VA: mail fecal occult blood tests directly to patients based
on EHR-derived criteria
 Beth Israel Deaconess: use prior colonoscopy records to send
reminder letters to PCPs and to patients, plus telephone call to Pts
 Northwestern: EHR-derived cohort sent letter from PCP, brochure,
colon cancer DVD, and follow-up phone call.
Results
 All interventions statistically significantly improved rates of colon
cancer screening.
Clinical Decision Support for Patients:
diabetes




Reference
 Lim S, et. al, Improved glycemic control without hypoglycemia in
elderly diabetic patients using the ubiquitous healthcare service, a
new medical information system. Diabetes Care. 2011 Feb;34(2):30813. .
Source
 Department of Medical Informatics, Seoul National University
Bundang Hospital, Seongnam, Korea.
Aim
 To assess the effect of patient self monitoring and CDSS on quality
and efficiency of care for elderly patients with type 2 diabetes.
Methods
 6-month randomized, controlled clinical trial involving 144 patients
aged >60 years.
 Participants were randomly assigned to receive routine care (control,
n = 48), to the self-monitored blood glucose (SMBG, n = 47) group, or
to the u-healthcare group (n = 49).
Clinical Decision Support for Patients:
diabetes


Reference
 Lim S, et. al, Improved glycemic control without hypoglycemia in
elderly diabetic patients using the ubiquitous healthcare service, a
new medical information system. Diabetes Care. 2011 Feb;34(2):30813. .
Methods, cont’d
 U-healthcare system = medical instructions given through the patient's
mobile phone.
 Patients receive a glucometer with a public switched telephone
network-connected cradle that automatically transfers test results to a
hospital-based server.
 Once the data are transferred to the server, an automated system, the
CDSS rule engine, generates and sends patient-specific messages by
mobile phone
 The primary end point was the proportion of patients achieving A1C
<7% without hypoglycemia at 6 months.
Clinical Decision Support for Patients:
diabetes



Reference
 Lim S, et. al, Improved glycemic control without hypoglycemia in
elderly diabetic patients using the ubiquitous healthcare service, a
new medical information system. Diabetes Care. 2011 Feb;34(2):30813.
Results
 After 6 months, mean A1C level was significantly decreased from 7.8 ±
1.3% to 7.4 ± 1.0% (P < 0.001) in the u-healthcare group and from 7.9
± 1.0% to 7.7 ± 1.0% (P = 0.020) in the SMBG group, compared with
7.9 ± 0.8% to 7.8 ± 1.0% (P = 0.274) in the control group.
 The proportion of patients with A1C <7% without hypoglycemia was
30.6% in the u-healthcare group, 23.4% in the SMBG group (23.4%),
and 14.0% in the control group (P < 0.05).).
Conclusions
 The CDSS-based u-healthcare service achieved better glycemic
control with less hypoglycemia than SMBG and routine care and may
provide effective and safe diabetes management in the elderly diabetic
patients.
Clinical Decision Support for Patients:
diabetes


Reference
 Lim S, et. al, Improved glycemic control without hypoglycemia in
elderly diabetic patients using the ubiquitous healthcare service, a
new medical information system. Diabetes Care. 2011 Feb;34(2):30813.
Importance
 Harbinger of a coming wave of intelligent cell phone apps for self care,
enabled by physiologic monitors
Clinical Decision Support for Patients:
Other conditions (4 RCTs)

References

Montori VM, et. al, Use of a decision aid to improve treatment
decisions in osteoporosis: the osteoporosis choice randomized
trial. Am J Med. 2011 Jun;124(6):549-56. [Mayo Clinic]

Mouttapa M, et. al, The Personal Nutrition Planner: a 5-week,
computer-tailored intervention for women. J Nutr Educ Behav.
2011 May-Jun;43(3):165-72. [Cal State, Fullerton]

Kelders SM, et. al, Effectiveness of a Web-based intervention
aimed at healthy dietary and physical activity behavior: a
randomized controlled trial about users and usage. J Med Internet
Res. 2011 Apr 14;13(2):e32. [University of Twente, Netherlands]

Wright A, et al, Randomized Controlled Trial of Health Maintenance
Reminders Provided Directly to Patients Through an Electronic
PHR. J Gen Intern Med. 2011 Sep 9. [Brigham & Womens]
Clinical Decision Support for Patients:
other (4 RCTs)


Interventions
 Mayo Clinic:Tailored pictographic 10-year fracture risk estimate,
absolute risk reduction with bisphosphonates, side effects, and out-ofpocket cost; control patients received a standard brochure.
 Cal State Fullerton: web app producing individualized nutrition
feedback based on initial on-line assessment.
 Twente Univ: Healthy Weight Assistant (HWA), a Web-based
intervention aimed at healthy dietary and physical activity behavior.
 Brigham & Womens: Tethered PHR with ‘e-Journal’ providing health
maintenance reminders
Results
 All interventions showed statistically significantly improved rates of
desired outcomes
CDSS Unintended Consequences




Reference
 Nanji KC et. al, Errors associated with outpatient computerized
prescribing systems. J Am Med Inform. Assoc. 2001 Jun 29.
Source
 Department of Anesthesia, Critical Care and Pain Medicine,
Massachusetts General Hospital, Boston, MA
Aims
 To report the frequency, types, and causes of errors associated with
outpatient computer-generated prescriptions
 To develop a framework to classify these errors to determine which
strategies have greatest potential for preventing them.
Methods
 Retrospective cohort study of 3850 computer-generated prescriptions
received by a commercial outpatient pharmacy chain across three
states over 4 weeks in 2008.
 Clinician panel reviewed the prescriptions to identify and classify
medication errors.
CDSS Unintended Consequences



Reference
 Nanji KC et. al, Errors associated with outpatient computerized
prescribing systems. J Am Med Inform. Assoc. 2001 Jun 29.
Methods, cont’d
 Primary outcomes were the incidence of medication errors; potential
adverse drug events, defined as errors with potential for harm; and
rate of prescribing errors by error type and by prescribing system.
Results
 Of 3850 prescriptions, 452 (11.7%) contained 466 total errors, of
which 163 (35.0%) were considered potential adverse drug events.
 Error rates varied by computerized prescribing system, from 5.1% to
37.5%. The most common error was omitted information (60.7% of all
errors).
 Results consistent with literature on manual handwritten prescription
error rates. The number, type, and severity of errors varied by
computerized prescribing system, suggesting that some systems may
be better at preventing errors than others.
CDSS Unintended Consequences



Reference
 Nanji KC et. al, Errors associated with outpatient computerized
prescribing systems. J Am Med Inform. Assoc. 2001 Jun 29.
Conclusions
 Implementing a computerized prescribing system without
comprehensive functionality and processes in place to ensure
meaningful system use does not decrease medication errors.
 The authors offer targeted recommendations on improving
computerized prescribing systems to prevent errors
Importance
 Technology must be well designed and thoughtfully implemented to
get intended effects
 The persistent tendency of media and opponents of technology to
draw conclusions that technology is not as good as manual
alternatives is a continuing challenge for those who design and
implement such systems
6 New CDSS RCTs showing no difference
for intervention vs. control
1.
2.
3.
4.
5.
6.
Linder JA, et. al, Electronic health record feedback to improve antibiotic
prescribing for acute respiratory infections. Am J Manag Care. 2010
Dec;16(12 Suppl HIT):e311-9. [Brigham & Womens, Boston]
Sequist TD, et. al, Electronic patient messages to promote colorectal
cancer screening: a randomized controlled trial. Arch Intern Med. 2011
Apr 11;171(7):636-41. Epub 2010 Dec 13. . [Brigham & Womens, Boston]
Eisenstein EL, et. al, Clinical and economic results from a randomized
trial of clinical decision support in a rural health network. Stud Health
Technol Inform. 2011;164:77-81. [Univ. Washington, Seattle]
Chung MH, et. al, A randomized controlled trial comparing the effects of
counseling and alarm device on HAART adherence and virologic
outcomes. P LoS Med. 2011 Mar;8. [Univ. Washington, Seattle]
Gill JM, et. al, Impact of EHR-based clinical decision support on
adherence to guidelines for patients on NSAIDs: a randomized
controlled trial. Ann Fam Med. 2011 Jan-Feb;9(1):22-30. [Delaware Valley]
Pellegrino VA, et. al, Computer based haemodynamic guidance system is
effective and safe in management of postoperative cardiac surgery
patients. Anaesth Intensive Care. 2011 Mar;39(2):191-201. [Alfred Hospital,
Melbourne]
Clinical Decision
Support for Providers
and Patients
Questions and Comments
Telemedicine
14 new RCTs published
November 2010 – October 2011
•6 cardiovascular diseases
•3 diabetes
•2 infectious diseases
•1 each: depression, multiple sclerosis,
cancer care
Telemedicine – cardiovascular diseases
3 RCTs

References
 Crossley GH, et. al, The CONNECT (Clinical Evaluation of
Remote Notification to Reduce Time to Clinical Decision) trial:
the value of wireless remote monitoring with automatic
clinician alerts. J Am Coll Cardiol. 2011 Mar 8;57(10):1181-9. [Univ
Tennessee Coll Med]

Christensen H, et. al, Home management of oral anticoagulation
via telemedicine versus conventional hospital-based treatment.
Telemed J E Health. 2011 Apr;17(3):169-76. Epub 2011 Jan 23.
[Univ Health Network, Toronto]

Nolan RP, et. al, Therapeutic benefit of preventive telehealth
counseling in the Community Outreach Heart Health and Risk
Reduction Trial. Am J Cardiol. 2011 Mar 1;107(5):690-6. Epub
2011 Jan 6.
Telemedicine – cardiovascular diseases


Interventions
 Univ Tenn: Implantable cardioverter-defibrillators with wireless
remote monitoring connect to automated clinician alerts.

Denmark: Home INR monitoring of oral anticoagulant therapy
combined with web-based expert system dose advisor

Toronto: Telehealth lifestyle counseling via online group
videoconferencing
Results
 All interventions yielded statistically significant increases in desired
outcome variables relative to control groups.
Telemedicine – diabetes (3 RCTs)

References
1.
Wakefield BJ, et. al, Effectiveness of home telehealth in
comorbid diabetes and hypertension: a randomized,
controlled trial. Telemed J E Health. 2011 May;17(4):254-61.
[Univ Iowa]
2.
Weinstock RS, et. al, Glycemic control and health disparities in
older ethnically diverse underserved adults with diabetes:
five-year results from the Informatics for Diabetes Education
and Telemedicine (IDEATel) study. Diabetes Care. 2011
Feb;34(2):274-9. [SUNY upstate]
3.
van Bastelaar KM, et. al, Web-based depression treatment for
type 1 and type 2 diabetic patients: a randomized, controlled
trial. Diabetes Care. 2011 Feb;34(2):320-5. Epub 2011 Jan 7.4.
[VU Univ Med Ctr, Netherlands]
Telemedicine - diabetes

Interventions




Univ Iowa: nurse-managed home telehealth device w/
standard telephone line for BP and BG data transmission
between the patient’s home and the study center.
SUNY/IDEATel: upload BG data and receive ‘home visits’
via video
Netherlands: Internet-delivered Cognitive Behavioral
Therapy for diabetics with depression.
Results


Statistically significant decreases in HbA1C levels for
studies focused on blood glucose control
Reduction in depressive symptoms in the CBT trials
Telemedicine – infectious diseases

References

Pop-Eleches C, et. al, Mobile phone technologies improve
adherence to antiretroviral treatment in a resource-limited
setting: a randomized controlled trial of text message
reminders. AIDS. 2011 Mar 27;25(6):825-34. [Columbia Univ]

Yardley L, et. al, Evaluation of a Web-based intervention
providing tailored advice for self-management of minor
respiratory symptoms: exploratory randomized controlled
trial. J Med Internet Res. 2010 Dec 15;12(4):e66. [Univ.
Southhampton, UK]
Telemedicine – infectious diseases

Interventions



Columbia Univ: SMS text messages to cell phones of HIV patients
in sub-Saharan Africa reminding them to take ARV meds.
Univ Southampton: “Internet Doctor” providing tailored advice on
self management of minor URIs.
Results

SMS weekly text messages improved ARV compliance

Improved understanding of illness and decreased clinic visits among
persons with URIs.
4 New Telemedicine RCTs showing no
difference for intervention vs. control
1.
Koehler F, et. al, Impact of remote telemedical
management on mortality and hospitalizations in
ambulatory patients with chronic heart failure: the
telemedical interventional monitoring in heart failure
study. Circulation. 2011 May 3;123(17):1873-80.
[Cardiovascular Center, Berlin]
2.
Konstam V, et. al, Health-related quality of life in a
multicenter randomized controlled comparison of
telephonic disease management and automated home
monitoring in patients recently hospitalized with heart
failure: SPAN-CHF II trial. J Card Fail. 2011 Feb;17(2):1517. [Univ. Massachusetts]
3.
Madsen LB, et. al, Economic evaluation of home blood
pressure telemonitoring: a randomized controlled trial.
Blood Press. 2011 Apr;20(2):117-25. Epub 2010 Nov 24.
[Aarhus Univ., Denmark]
4 New Telemedicine RCTs showing no
difference for intervention vs. control
4.
Miller DM, et. al, Web-based self-management for
patients with multiple sclerosis: a practical,
randomized trial. Telemed J E Health. 2011 JanFeb;17(1):5-13. [Cleveland Clinic]
Telemedicine
Questions and Comments
Practice of Informatics
Practice of Informatics:
issues related to EHR adoption (5 articles)

References
1.
Linder JA et. al, Clinician characteristics and use of novel
electronic health record functionality in primary care. J Am Med
Inform Assoc. 2011 Sep 7. [Brigham & Womens]
Key finding: contrary to conventional wisdom, busy clinicians with
complex patients are more likely to use new EHR functionality than
peers who are less busy
2.
Wilcox AB, Chen YH, Hripcsak G. Minimizing electronic health
record patient-note mismatches. J Am Med Inform Assoc. 2011
Jul-Aug;18(4):511-4. [Columbia Univ.]
Key finding: wrong patient notes in EHRs occur at frequency of
about 0.5% (1 in 200) but can be reduced to 1:300 by pop-up
alerting.
Practice of Informatics:
issues related to EHR adoption (5 articles)

References
3.
Adler-Milstein J, Bates DW, Jha AK. A survey of health
information exchange organizations in the United States:
implications for meaningful use. Ann Intern Med
2011;154(10):666-671. [Brigham & Womens]
Key findings: Only 13 of 75 operational RHIOs supported stage 1
meaningful use (covering 3% of hospitals and 0.9% of practices),
and none met an expert-derived definition of a comprehensive
RHIO. Overall, 50 of 75 RHIOs (67%) did not meet the criteria for
financial viability.
4.
Sittig DF, Singh H. Defining health information technologyrelated errors: new developments since to err is human. Arch
Intern Med. 2011 Jul 25;171(14):1281-4. [Univ Texas Houston]
Key findings: definitions of types of HIT-related errors and a
sociotechnical approach to understand causes of errors
Practice of Informatics:
issues related to EHR adoption (5 articles)

References
5.
Goodman KW et. al, Challenges in ethics, safety, best practices,
and oversight regarding HIT vendors, their customers, and
patients: a report of an AMIA special task force. Journal of the
American Medical Informatics Association 2011;18(1):77-81. [Univ.
Miami]
Key findings: patient safety trumps corporate liability and IP
concerns. Transparency and standards of corporate conduct
needed. Institutions should provide ethics education to purchasers
and users.
Practice of Informatics: clinical data mining

References

Tatonetti, NP et. al, Detecting drug interactions from adverseevent reports: interaction between paroxetine and pravastatin
increases blood glucose levels. Clinical Pharmacology &
Therapeutics, 90(1), 133–142. doi:10.1038/clpt.2011.83 [Stanford,
Harvard, Vanderbilt]

Holmes AB, et. al, Discovering disease associations by
integrating electronic clinical data and medical literature. PLoS
One. 2011;6(6):e21132. Epub 2011 Jun 23. [Columbia Univ]
Practice of Informatics


Methods
 Stanford: Look for glycemic effects in FDA Adverse Event database,
find higher than expected BG with co-administration of two drugs, then
verified observed associations by data mining 3 institution-wide EMR
data warehouses.
 Columbia: NLP + statistical approach to finding co-morbidities of rare
diseases (Kaposi sarcoma, toxoplasmosis, Kawasaki disease) from
data mining of literature (PubMed + Wikipedia) combined with local
EHR data.
Results
 Stanford-Harvard-Vanderbilt consortium found unique paroxetine +
pravastatin induced hyperglycemia not associated with either drug
administered alone.
 Columbia found new association between Kawasaki disease and
autism.
 Approaches published and available for use by others.
Practice of Informatics



References
 Jameson E, et. al, Electronic gaming as pain distraction. Pain Res
Manag. 2011 Jan-Feb;16(1):27-32.
Source
 Department of Psychological Medicine, Dunedin School of Medicine,
Dunedin, New Zealand.
Methods
 60 participants were asked to submerge their hand in cold (2°C) water
for as long as they could tolerate.
 They did this with no distraction, and then with active (electronic
gaming system) and passive (television) distraction, in randomly
assigned order.
 Tolerance time, pain intensity ratings and task absorption ratings were
measured for each condition. .
Practice of Informatics



References
 Jameson E, et. al, Electronic gaming as pain distraction. Pain Res
Manag. 2011 Jan-Feb;16(1):27-32.
Results
 Participants in both experiments had a significantly higher pain
tolerance and reported less pain with the active distraction compared
with passive or no distraction.
 They also had more enjoyment, less anxiety and greater reduction in
pain with active distraction .
 These experiments offer further support for the use of electronic
games as a method of pain control..
Importance
 More evidence explaining why your kids ignore you while playing video
games
Practice of Informatics




Reference
 Müller BH, et. al, One-session computer-based exposure treatment for
spider-fearful individuals--efficacy of a minimal self-help intervention in
a randomised controlled trial. J Behav Ther Exp Psychiatry. 2011
Jun;42(2):179-84.
Source
 Department of Clinical Psychology and Psychotherapy, University of
Basel, Missionsstrasse 60/62, CH-4055 Basel, Switzerland.
Aim
 To investigate the efficacy of one-session computer-based exposure
(CBE) as a self-help treatment for spider-fearful individuals..
Methods
 Spider-fearful participants in a CBE group underwent one 27-min
session of standardised exposure to nine fear-eliciting spider pictures
 Treatment outcome was compared to spider-fearful control
participants exposed to nine neutral pictures.
 Fear reduction was quantified on a subjective level by the Fear of
Spiders Questionnaire (FSQ) and complemented with a behavioural
approach test (BAT)..
Practice of Informatics



Reference
 Müller BH, et. al, One-session computer-based
exposure treatment for spider-fearful individuals
--efficacy of a minimal self-help intervention
in a randomised controlled trial. J Behav Ther
Exp Psychiatry. 2011 Jun;42(2):179-84. Epub 2010 Dec 14.
Results
 Compared to control participants, CBE participants showed greater
fear reduction from pre- to posttreatment on both the subjective level
(FSQ) and the behavioural level (BAT).
 Moreover, in contrast to the control group, the obtained subjective fear
reduction effect remained stable in the CBE group at 1-month followup.
 These findings highlight the role of computer-based self-help as a
minimal but effective intervention to reduce fear of spiders..
Importance
 Self evident ;-).
Practice of Informatics
Questions and Comments
New Literature Highlights:
Bioinformatics and
Computational Biology
Human Health and Disease
 The practice of bioinformatics

Bioinformatics: Human Health & Disease – cancer

References
 Tiacci E, et. al, BRAF mutations in hairy-cell leukemia. N Engl J
Med. 2011 Jun 16;364(24):2305-15. Epub 2011 Jun 11. [Univ.
Perugia, Italy]
Key findings: Whole exome sequencing of 47 patients found a
specific mutation (BRAF V600E) that is pathognomonic and causal
for the disease.

Pasqualucci L et al. Analysis of the coding genome of diffuse
large B-cell lymphoma. Nat Genet. 2011 Jul 31;43(9):830-7. doi:
10.1038/ng.892. [Columbia Univ.]
Key findings: Average nonHodgkins lymphoma case has 30
expressed clonal gene alterations, few in common across cases.
Bioinformatics: Human Health & Disease – cancer

References
 Chang HH, et. al, A transcriptional network signature
characterizes lung cancer subtypes. Cancer. 2011 Jan
15;117(2):353-60. doi: 10.1002/cncr.25592. [Harvard-MIT
informatics]
Key findings: developed a classifier based on gene expression
profiles of 111 lung cancer specimens that distinguishes
Adenocarcinoma from Squamous Cell Carcinoma and points to 3
genes that are responsible. (Last work of Marco Ramoni)

Kamper P, et. al, Proteomic analysis identifies galectin-1 as a
predictive biomarker for relapsed/refractory disease in
classical Hodgkin lymphoma. Blood. 2011 Jun 16;117(24):663849. Epub 2011 Apr 19.
Key findings: Proteomic analysis of 14 Hodgkins lymphoma found
a common protein biomarker that predicts treatment response and
survival
Bioinformatics: Human Health & Disease – cancer

References
 Stein RA, et. al, Epigenetics--the link between infectious
diseases and cancer. JAMA. 2011 Apr 13;305(14):1484-5.
A good review of the relationship of the microbiome and cancer in
humans.
Bioinformatics: Human Health & Disease – CNS

References
 Gilman SR et. al, Rare de novo variants associated wih autism
implicate a large functional network of genes involved in
formation and function of synapses. Neuron. 2011 Jun
9;70(5):898-907. [Columbia Univ.]
Key findings: Used analysis of rare genetic variants to show
autism linked to synapse development, and explain why autism is
more common in males than females.

Föcking M, et. al, Common proteomic changes in the
hippocampus in schizophrenia and bipolar disorder and
particular evidence for involvement of cornu ammonis regions
2 and 3. Arch Gen Psychiatry. 2011 May;68(5):477-88. [Royal
College of Surgeons, Dublin Ireland]
Key findings: Used proteomics to find distinctive abnormal
proteins in the hippocampus in schizophrenia and bipolar disorder.
The Practice of Bioinformatics

Reference

Altman RB, et. al, Pharmacogenomics: "noninferiority" is sufficient
for initial implementation. Clin Pharmacol Ther. 2011
Mar;89(3):348-50. [Stanford Univ.]
Key findings: because of the combinatorics of rare variants predict
that conclusive evidence of gene-drug effects will often not be
found with the certainty levels used in clinical trials, standard for
using pharmacogenetic data should be whether it is no worse than
than standard prescribing.
Computational Biology
and Bioinformatics
Questions and Comments
Top Five List of
Notable Events
in the Past 12 months
“Top Five” Events
5. Total population of planet earth passes 7 billion
“Top Five” Events
5. Total population of planet earth passes 7 billion
4. December 10, 2010. Release of PCAST Health Information
Technology Report
“Top Five” Events
5. Total population of planet earth passes 7 billion
4. December 10, 2010. Release of PCAST Health Information
Technology Report
3. National Library of Medicine 175th Anniversary
“Top Five” Events
5. Total population of planet earth passes 7 billion
4. December 10, 2010. Release of PCAST Health Information
Technology Report
3. National Library of Medicine 175th Anniversary
2. May 2011. First payments for EHR Meaningful Use
And the #1 top event of
2010 is…
“Top Five” Events
5. Total population of planet earth passes 7 billion
4. December 10, 2010. Release of PCAST Health Information
Technology Report
3. National Library of Medicine 175th Anniversary
2. May 2011. First payments for EHR Meaningful Use
1. September 2011. American Board of Medical Specialties
approves Certification in Clinical Informatics
Content for this session is at:
http://faculty.washington.edu/dmasys/YearInReview
including citation lists and links
and this PowerPoint