A Randomized Trial of Electronic Clinical Reminders to Improve Medication Laboratory Monitoring

Matheny, Michael E.; Sequist, Thomas D.; Seger, Andrew C.; Fiskio, Julie M.; Sperling, Michael R.; Bugbee, Donald; Bates, David W.; Gandhi, Tejal K.

doi:10.1197/jamia.m2602

Cited by 47 publications

(48 citation statements)

References 38 publications

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“…Positive effects were also found by Feldstein et al 20 evaluating the effect of CCDSSs reminding physicians of 10 possible drug-laboratory interactions in Kaiser Permanente primary care practices. That this trend is not absolute is illustrated by the study by Matheny et al 30 studying a similar CCDSS aimed at 14 drug-laboratory interactions within the Longitudinal Medical Record at Brigham & Women's Hospital. They could not report a significant effect on appropriateness of testing, but an important note to be made is that the baseline rates of overdue testing were already very low before the implementation of the CCDSS, hence leaving very little margin for improvement.…”

Section: Discussionmentioning

confidence: 92%

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The Effects of Computerized Clinical Decision Support Systems on Laboratory Test Ordering: A Systematic Review

Delvaux¹,

Thienen²,

Heselmans³

et al. 2017

Archives of Pathology &Amp; Laboratory Medicine

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Context.-Inappropriate laboratory test ordering has been shown to be as high as 30%. This can have an important impact on quality of care and costs because of downstream consequences such as additional diagnostics, repeat testing, imaging, prescriptions, surgeries, or hospital stays.Objective.-To evaluate the effect of computerized clinical decision support systems on appropriateness of laboratory test ordering.Data Sources.-We used MEDLINE, Embase, CINAHL, MEDLINE In-Process and Other Non-Indexed Citations, Clinicaltrials.gov, Cochrane Library, and Inspec through December 2015. Investigators independently screened articles to identify randomized trials that assessed a computerized clinical decision support system aimed at improving laboratory test ordering by providing patientspecific information, delivered in the form of an on-screen management option, reminder, or suggestion through a computerized physician order entry using a rule-based or algorithm-based system relying on an evidence-based knowledge resource. Investigators extracted data from 30 papers about study design, various study characteristics, study setting, various intervention characteristics, involvement of the software developers in the evaluation of the computerized clinical decision support system, outcome types, and various outcome characteristics.Conclusions.-Because of heterogeneity of systems and settings, pooled estimates of effect could not be made. Data showed that computerized clinical decision support systems had little or no effect on clinical outcomes but some effect on compliance. Computerized clinical decision support systems targeted at laboratory test ordering for multiple conditions appear to be more effective than those targeted at a single condition.(Arch Pathol Lab Med. 2017;141:585-595; doi: 10.5858/ arpa.2016-0115-RA) A fter years of persistent increase, laboratory test ordering has become the highest-volume medical act. In the United States 1 and Europe, 55 the annual increase in the use of laboratory tests averaged around 5% during the last decade. Medicare 2 spending for clinical laboratory testing has peaked at almost $10 billion, accounting for 1.7% of the total health care budget. However, it has been estimated that when including non-Medicare spending, the figures for the United States are up to 7 times higher.3 In Europe, laboratory spending accounted for 1.8% of total health care spending in 2012, totaling E17 billion. Overuse of laboratory test ordering has been shown to be at 20%. 4 Even though reducing the overuse of laboratory testing may not seem very important in relation to the whole of health care spending, the true benefits are to be found in reducing downstream costs.5 These downstream activities include additional diagnostics such as repeat testing or imaging, prescriptions, surgeries, and hospital stays. Besides reducing costs, improving appropriate laboratory test ordering improves the quality of care by avoiding false-positive results and accidental findings of unknown significance, 6 and is not ass...

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Section: Discussionmentioning

confidence: 92%

“…[20][21][22][24][25][26][27][28][29][30][31][32][33][34][35][36][37] Eight studies (35%) were conducted in Europe. 23,[38][39][40][41][42][43][44][45] One study (4%) was conducted in Europe and Australia.…”

Section: Included Studiesmentioning

confidence: 99%

The Effects of Computerized Clinical Decision Support Systems on Laboratory Test Ordering: A Systematic Review

Delvaux¹,

Thienen²,

Heselmans³

et al. 2017

Archives of Pathology &Amp; Laboratory Medicine

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“…While prior literature either evaluates only physician test ordering 5,6 or overall test completion, 8,9 rarely are both reported. 20,40 Most studies have reported only completion rates, usually determined from claims data, 41 while we have separated outcomes into test ordering and patient completion of ordered tests.…”

Section: Discussionmentioning

confidence: 99%

“…Some studies report clinician ordering only, 5,6 while others report overall test completion rates. [7][8][9][10][11] In addition, little information is available about why patients fail to complete ordered laboratory tests. Patient understanding of the reason for testing may affect test adherence, as suggested by one study of warfarin.…”

Section: Introductionmentioning

confidence: 99%

Patient Completion of Laboratory Tests to Monitor Medication Therapy: A Mixed-Methods Study

et al. 2012

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BACKGROUND:Little is known about the contribution of patient behavior to incomplete laboratory monitoring, and the reasons for patient non-completion of ordered laboratory tests remain unclear. OBJECTIVE: To describe factors, including patientreported reasons, associated with non-completion of ordered laboratory tests. DESIGN: Mixed-Methods study including a quantitative assessment of the frequency of patient completion of ordered monitoring tests combined with qualitative, semi-structured, patient interviews. PARTICIPANTS: Quantitative assessment included patients 18 years or older from a large multispecialty group practice, who were prescribed a medication requiring monitoring. Qualitative interviews included a subset of show and no-show patients prescribed a cardiovascular, anticonvulsant, or thyroid replacement medication. MAIN MEASURES: Proportion of recommended monitoring tests for each medication not completed, factors associated with patient non-completion, and patientreported reasons for non-completion. KEY RESULTS: Of 27,802 patients who were prescribed one of 34 medications, patient non-completion of ordered tests varied (range: 0-24 %, by drug-test pair). Factors associated with higher odds of test noncompletion included: younger patient age (< 40 years vs. ≥ 80 years, adjusted odds ratio [AOR] 1.52, 95 % confidence interval [95 % CI] 1.27-1.83); lower medication burden (one medication vs. more than one drug, AOR for non-completion 1.26, 95 % CI 1.15-1.37), and lower visit frequency (0-5 visits/year vs. ≥ 19 visits/ year, AOR 1.41, 95 % CI 1.25 to 1.59). Drug-test pairs with black box warning status were associated with greater odds of non-completion, compared to drugs without a black box warning or other guideline for testing (AOR 1.91,. Qualitative interviews, with 16 no-show and seven show patients, identified forgetting as the main cause of non-completion of ordered tests. CONCLUSIONS:Patient non-completion contributed to missed opportunities to monitor medications, and was associated with younger patient age, lower medication burden and black box warning status. Interventions to improve laboratory monitoring should target patients as well as physicians.

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“…Cross-sectional studies of the impact of EHRs on quality have shown inconsistent results 3–12. No randomized clinical trial or longitudinal study using a direct measure of clinical quality has been conducted to test this assumption.…”

Section: Introductionmentioning

confidence: 99%

The adoption of an electronic health record did not improve A1c values in Type 2 diabetes

Burke

Becher

Hoang

et al. 2016

jhi

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AbsTrAcTbackground A major justification for the clinical adoption of electronic health records (EHRs) was the expectation that it would improve the quality of medical care. No longitudinal study has tested this assumption. Objective We used hemoglobin A1c, a recognized clinical quality measure directly related to diabetes outcomes, to assess the effect of EHR use on clinical quality. Methods We performed a five-and-one-half-year multicentre longitudinal retrospective study of the A1c values of 537 type 2 diabetic patients. The same patients had to have been seen on at least three occasions: once approximately six months prior to EHR adoption (before-EHR), once approximately six months after EHR adoption (after-EHR) and once approximately five years after EHR adoption (five-years), for a total of 1,611 notes. results The overall mean confidence interval (CI) A1c values for the before-EHR, after-EHR and five-years were 7.07 (6.91 -7.23), 7.33 (7.14 -7.52) and 7.19 (7.06 -7.32), respectively. There was a small but significant increase in A1c values between before-EHR and after-EHR, p = .04; there were no other significant differences. There was a significant decrease in notes missing at least one A1c value, from 42% before-EHR to 16% five-years (p < .001). conclusion We found that based on patient's A1c values, EHRs did not improve the clinical quality of diabetic care in six months and five years after EHR adoption. To our knowledge, this is the first longitudinal study to directly assess the relationship between the use of an EHR and clinical quality.

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A Randomized Trial of Electronic Clinical Reminders to Improve Medication Laboratory Monitoring

Abstract: We identified high rates of appropriate laboratory monitoring, and electronic reminders did not significantly improve these monitoring rates. Future studies should focus on settings with lower baseline adherence rates and alternate drug-laboratory combinations.

Cited by 47 publications

References 38 publications

The Effects of Computerized Clinical Decision Support Systems on Laboratory Test Ordering: A Systematic Review

The Effects of Computerized Clinical Decision Support Systems on Laboratory Test Ordering: A Systematic Review

Patient Completion of Laboratory Tests to Monitor Medication Therapy: A Mixed-Methods Study

The adoption of an electronic health record did not improve A1c values in Type 2 diabetes

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