Shawn J. Gagne, BA

 While the 2011 implementation of “meaningful use” legislation for certified electronic health records (EHRs) promises to change quality reporting by overcoming data capture issues affecting quality measurement, the magnitude of this effect is unclear. We compared the measured quality of laboratory monitoring of Healthcare Effectiveness Data and Information Set (HEDIS) medications based on specifications that (1) include and exclude patients hospitalized in the measurement year and (2) use physician test orders and patient test completion.

 Among patients 18 years and older in a large multispecialty group practice utilizing a fully implemented EHR between January 1, 2008, and July 31, 2008, we measured the prevalence of ordering and completion of laboratory tests monitoring HEDIS medications (cardiovascular drugs [angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, digoxin, and diuretics] and anticonvulsants [carbamazepine, phenobarbital, phenytoin, and valproic acid]).

 Measures excluding hospitalized patients were not statistically significantly different from measures including hospitalized patients, except for digoxin, but this difference was not clinically significant. The prevalence of appropriate monitoring based on test orders typically captured in the EHR was statistically significantly higher than the prevalence based on claims-based test completions for cardiovascular drugs.

 HEDIS quality metrics based on data typically collected from claims undermeasured quality of medication monitoring compared to EHR data. The HEDIS optional specification excluding hospitalized patients from the monitoring measure does not have a significant impact on reported quality. Integration of EHR data into quality measurement may significantly change some organizations’ reported quality of care.

	Integration of electronic health record (EHR) data into quality measurement may significantly change some organizations’ reported quality of care.

		For example, measuring laboratory monitoring quality from claims data (test completion) underestimates care quality compared with measurements based on EHR data (physician test ordering).

		If quality-of-care measurements improve concurrent with “meaningful use” implementation, it may be difficult to discern whether this change reflects true quality improvements or measurement changes.

		One strategy to address this issue is to measure both claims only and claims plus EHRbased measures concurrently so that quality changes can be interpreted going forward.

 prompted significant efforts toward the pursuit of high-quality healthcare. As a result, major investments to improve healthcare quality have focused on 2 areas: (1) the development and public reporting of quality-of-care measures and (2) the promotion and adoption of electronic health records (EHRs).

 The synergy of these 2 concurrent efforts was recently accelerated by the 2011 implementation of incentive payments for the meaningful use of certified EHR technology under the 2009 American Recovery and Reinvestment Act

; this synergy will have an important impact on healthcare in the United States.

	The Centers for Medicare & Medicaid Services’ stage 1 rollout of “meaningful use” criteria for EHR in January 2011 aimed to reduce disparities in EHR use across healthcare deliverers by providing monetary incentives for EHR adoption.

 While one of the goals in promoting the widespread adoption of EHR is to improve quality of care,

 there is evidence to suggest that expanded EHR availability and meaningful data integration may impact quality based on changes in data capture and measurement, and not based on actual improvements in healthcare quality.

	For example, prior studies have shown that quality measures calculated from administrative claims alone are different from measures that incorporate medical record data.

 As a result, “hybrid” administrative-medical record—based calculation methods were incorporated into some, but not all, Healthcare Effectiveness Data and Information Set (HEDIS) performance measures.

 The implication is that healthcare enterprises without EHRs are disadvantaged,

 while those equipped to readily capture medical record data for quality reporting have an advantage by being able to report higher numbers for performance measures than those using only administrative claims. While this phenomenon has been described for cancer screening and vaccination rates,

 it has not been examined for the quality measurement of the laboratory monitoring of medications.

 and failure to monitor high-risk medications is one of the leading factors contributing to adverse drug events,

 the National Committee for Quality Assurance included medication monitoring measures in HEDIS in 2006.

 These standards recommend laboratory monitoring of high-risk, narrow therapeutic window medications, including angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs), digoxin, diuretics, and anticonvulsants (phenobarbital, phenytoin, valproic acid, and carbamazepine)

 based on administrative data only. Aware of the difficulties with data capture across transitions of care from the hospital to the ambulatory setting, HEDIS optionally specifies that measurement may be affected by population selection (ie, excluding or including hospitalized patients whose hospital laboratory tests are not consistently reported to ambulatory medical records or claims), but it does not specify that measurement may be affected by the source of the data.

	Because the magnitude of data source and population selection effects is unclear, we conducted this study to assess the ordering and completion of laboratory tests for high-risk HEDIS medications in a large multispecialty group practice to compare the reported quality of monitoring based on (1) 2 HEDIS specifications for the population (including and excluding patients hospitalized in the measurement year) and (2) 2 outcome measures (physician test orders vs actual completion of tests). With the federal investment promising to eliminate barriers to EHR adoption, our findings have implications for quality-of-care reporting and measurement, and will inform some expected developments resulting from the EHR meaningful use legislation.

	This study was conducted in a large multispecialty group practice that provides the majority of medical care to members of a closely associated New England—based health plan. The group practice employs 250 outpatient physicians at 30 ambulatory clinic sites. The practice uses the EpicCare Ambulatory EHR system and provides medical care to approximately 180,000 individuals. Patients had to be continuously enrolled during the observation period and not residing in a long term care facility. Data about medication exposure were derived from the prescription drug claims of the health plan. Data about laboratory test ordering and completion were derived from the multispecialty group EHR. The age and sex characteristics of the study population were similar to those of the general population of the United States in 2000

: 54% of the adults were female and 36% were over 65 years of age. The group practice has only recently begun to capture race and has incomplete data, but the health plan’s market research indicates a patient racial mix consistent with the plan’s catchment area, which includes whites (79%), Hispanics (12%), African Americans (5%), and other races (4%).

HEDIS Drugs and Recommended Monitoring Tests

	We used drug dispensing claims from January 1, 2008, to July 31, 2008, to identify the first dispensing of 1 of the highrisk medications for a patient on or after January 1, 2008. We used drug dispensing claims from January 1, 2007, to December 31, 2007, as a look-back period in order to identify patients who were taking a drug for at least 180 days, as specified by HEDIS guidelines; we included only patients with evidence of another drug dispensing in the 180 days prior to January 1, 2008 (

). For the study drugs, appropriate annual monitoring was defined as receipt of a serum potassium test and either a serum creatinine or blood urea nitrogen test for ACE inhibitors/ARBs, digoxin, and diuretics, and receipt of a test for anticonvulsant drug serum concentration for the anticonvulsants. Test ordering and test completion were defined as having occurred if there was at least 1 recommended test order and test completion for each specific drug-test pair either 180 days before or after the index dispensing in 2008. To test the effect of population specification on reported quality as specified by HEDIS,

 we created 2 estimates for each drug-test combination: one based on the entire study population and a second excluding patients with any hospitalization in the observation year. To test the effect of outcome specification on reported quality, we also created estimates based on test completion.

 test to compare differences in monitoring based on the 2 HEDIS specifications for the population (including and excluding patients hospitalized in the measurement year) and the 2 outcome measures (physician test orders vs actual test completion). All analyses were conducted with SAS version 9.2 (SAS Institute Inc, Cary, North Carolina). This study was approved by the institutional review boards of our research institution and the participating group practice.

Test Ordering by Clinicians Including and Excluding Hospitalized Patients

	Approximately 10% of each population of medication users had a hospitalization in the observation year, except digoxin users, approximately 20% of whom were hospitalized (

). There were no statistically significant differences in the prevalence of appropriate test monitoring when we compared estimates based on the sample including hospitalized patients with estimates based on the sample excluding hospitalized patients. For example, 93.9% of hospitalized patients prescribed digoxin had appropriate test monitoring, compared with 92.3% of patients who were not hospitalized (

Test Ordering by Clinicians Compared With Overall Test Completion

	The prevalence of test completion for all drugs was lower than that of physician test ordering because patient adherence to test orders ranged from 85.6% to 93.3% (data not shown). When we examined the sample that included hospitalized patients and compared physician test orders with overall test completion, there were statistically significant differences between these 2 measures for the cardiovascular drugs, but not the anticonvulsants. For example, for diuretics, 92.3% of physicians ordered the appropriate monitoring test, but only 80.2% of all indicated tests were completed (

). These differences were statistically insignificant when the drug was less commonly used.

	This study examines 2 aspects of HEDIS quality measurement for medication monitoring. We found that the selection of the outcome measure can affect the reported quality of a physician. In contrast to HEDIS recommendations, it does not appear that the decision of whether or not to include hospitalized patients in the measure estimates has a significant impact. These results have implications for quality-of-care measurement.

	Our findings are consistent with those of other studies that indicate that measured performance varies depending upon the source of information, whether administrative data, the EHR, or a combination.

 Institutions relying solely on administrative data may underreport HEDIS quality- of-care measures.

 For example, among 283 commercial health plans that submitted HEDIS data, 178 had a greater than 10% point difference in the prevalence of postmyocardial infarction beta-blocker use when the HEDIS measure used administrative plus medical record data compared with use of administrative data alone.

 We build on this literature by showing that it is important to disentangle physician ordering behavior from patient test completion behavior. For example, if a physician appropriately ordered a test 100% of the time, but patients failed to complete the test 20% of the time, the physician would be judged to have an 80% monitoring rate, the same as a physician who ordered a test only 80% of the time but had patients who always completed tests. If HEDIS measurements are meant to be reflective of physician quality

 then this distinction is important for quality reporting. Alternatively, if the HEDIS measure is meant to reflect the practice’s ability to achieve monitoring through proper education, provision of convenient testing, and following up with no-shows, then using administrative claims to report completion only may be appropriate.

	Although HEDIS has taken the precaution of preventing potential undermeasurement of monitoring due to hospitalization, it appears that going to the effort to exclude hospitalized patients from outcome measurement does not make a significant difference. Our overall findings of the rates of testing are consistent with those of several previous studies that report variable, and sometimes low, rates of laboratory monitoring of anticonvulsant medications. For example, previous studies report that monitoring for anticonvulsant valproic acid ranged from 39.8% to 62%,

 while monitoring ranged from 60% to 70% for cardiovascular and nonstatin lipid-lowering drugs

 shows that healthcare systems differ in their measurement of the medication-monitoring metric, basing their estimates on test completion rates from claims data

 in some cases and on physician test orders from EHRs in other cases.

 Most administrative claims—based studies include only test completion because test ordering might depend on the availability of electronic medical record data. Ordering rates necessarily exceed completion rates, so using completion rates based on administrative claims data may underreport physician performance when compared with using ordering rates based on electronic medical record data. Ordering rates, however, are likely unavailable for many providers that do not have an EHR. Because HEDIS standards do not specify which measure—ordering or completion&mdash;is required, there is the potential to compare health plan performance on an apples-to-oranges basis. A key issue is that the differences in the data elements available, and a shift in the data capture abilities due to meaningful use, will likely affect the reporting of quality measurements. Therefore, it will be important to understand whether temporal trends in quality of care are due to actual improvements in care or due to changes in quality measurement.

	Limitations of our study deserve to be noted. First, our study was conducted in a single multispecialty group practice, limiting the generalizability of our findings to other settings. Second, we were unable to confirm patient adherence to drugs and therefore were unable to identify patients who did not complete tests because they were no longer using the medication.

	The widespread use of EHR data for quality measurement has been delayed due to a variety of problems with interoperability, lack of standardized coding schema, and the inability to retrieve some critical data electronically. Integration of EHR data into quality metrics is only now being actively pursued by a large section of the healthcare community.

 Our findings, taken together with the study of Pawlson et al showing that quality ranks of health plans based on HEDIS hybrid performance measures differed from their ranks based on administrative-only data,

 suggest that reported quality of care will improve over time as integrated administrative/EHR-based measures are more widely used. Further, if quality-of-care measurements improve concurrently with EHR rollout, it will be difficult to interpret whether that reflects improvements in healthcare quality or simply differences in quality measurement methodology. One strategy to address this issue may be to measure both administrative-only and administrative-plus-EHR measures concurrently so that we can discern the changes going forward.

	The authors would like to acknowledge the contributions of Devi Sundaresan, MA.

 From University of Massachusetts Medical School (JT, TSF, SHF, JHG), Worcester; Meyers Primary Care Institute (JT, TSF, SJG, DJP, LDG, JHG), Worcester, MA.

 This study was funded by grants R18 HS17203, R18 HS17817, and R18 HS17906 from the Agency for Healthcare Research and Quality.

 The authors (JT, TSF, SHF, SJG, DJP, LDG, JHG) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Articles

Shifting from claims to integrated electronic health records to calculate quality metrics will improve reported quality attributable to data capture changes, not true quality improvements.

Quality Measurement of Medication Monitoring in the Meaningful Use Era

 To develop guidelines to monitor high-risk medications and to assess the prevalence of laboratory testing for these medications among a multispecialty group practice.

We developed guidelines for the laboratory monitoring of high-risk medications as part of a patient safety intervention trial. An advisory committee of national experts and local leaders used a 2-round Internet-based Delphi process to select guideline medications based on the importance of monitoring for efficacy, safety, and drug—drug interactions. Test frequency recommendations were developed by academic pharmacists based on a literature review and local interdisciplinary consensus. To estimate the potential effect of the planned intervention, we determined the prevalence of high-risk drug dispensings and laboratory testing for guideline medications between January 1, 2008, and July 31, 2008.

Consensus on medications to include in the guidelines was achieved in 2 rounds. Final guidelines included 35 drugs or drug classes and 61 laboratory tests. The prevalence of monitoring ranged from less than 50.0% to greater than 90.0%, with infrequently prescribed drugs having a lower prevalence of recommended testing (P <.001 for new dispensings and P <.01 for chronic dispensings, nonparametric test for trend). When more than 1 test was recommended for a selected medication, monitoring within a medication sometimes differed by greater than 50.0%.

Even among drugs for which there is general consensus that laboratory monitoring is important, the prevalence of monitoring is highly variable. Furthermore, infrequently prescribed medications are at higher risk for poor monitoring.

This article adds to the existing literature and informs clinical decisions in the following ways:

By describing a process to develop guidelines for the laboratory monitoring of high-risk medications within a multispecialty group practice. 

By estimating the potential effect of an intervention to improve laboratory testing of high-risk medications by reporting the use frequency of high-risk medications requiring monitoring in 2008 and the prevalence of test completion for high-risk drugs in 2008. 

By demonstrating that infrequently used medications have lower rates of recommended laboratory test monitoring.

Drug-induced injury is common. Independent risk factors for adverse drug events in the ambulatory setting include advanced age, multiple comorbid conditions, and the use of high-risk medications requiring close monitoring. For example, failure to appropriately monitor older patients receiving drug therapy accounts for 36% of preventable adverse drug events in the ambulatory setting.

Efforts to improve monitoring within organizations are hampered by a lack of comprehensive and specific guidelines for the laboratory monitoring of high-risk medications.

 Monitoring guidelines for selected drugs exist in recommendations of organizations such as the American Heart Association guidelines to manage heart failure,

 the American Geriatrics Society Assessing Care of Vulnerable Elders medication quality indicators,

 and the National Quality Forum—endorsed measures.

A nationwide baseline monitoring assessment study

 developed more comprehensive guidelines for medications used through 2000, among which a subset of 14 were updated and adopted for an intervention trial between 2002 and 2003 within a single health maintenance organization.

We sought to develop an updated and comprehensive list of drugs requiring laboratory monitoring for an electronic medical record—embedded clinical decision support intervention at a multispecialty group practice for medications in clinical use during 2008. The intent of the updated guidelines was to include drugs introduced to the market since December 2000, the date of the literature review for the original drug and laboratory monitoring recommendations published by Raebel et al

 in 2005, as well as to update laboratory test and frequency recommendations based on 2007 changes in monitoring recommendations.

 Herein, we describe the development of guidelines to monitor high-risk medications in the ambulatory setting using a 2-step consensus-based approach, including a national expert advisory panel and local leaders to select candidate medications for monitoring and to determine the frequency of laboratory monitoring. To estimate the potential effect of our guidelines on actual practice, we determined the use frequency of the guideline drugs and the prevalence for each of the recommended laboratory tests. The specific objectives of this study were (1) to develop recommendations to guide the monitoring of high-risk medications in the ambulatory setting, (2) to assess the use prevalence of candidate medications for monitoring, and (3) to determine completion of recommended testing for medications dispensed among the patient population.

This study was conducted in a large multispecialty group practice that provides most medical care to members of a closely associated health plan based in the New England area of the United States. The group practice employs 250 outpatient clinicians at 30 ambulatory clinic sites. The practice uses the EpicCare Ambulatory (Epic, Verona, WI) electronic medical record system and provides medical care to approximately 180,000 individuals. For this analysis, we included patients if they received care from the multispecialty group practice, were 18 years or older, and obtained insurance coverage from the health plan between January 1, 2007, and July 31, 2008. Patients had to be continuously enrolled during the observation period and not residing in a long-term care facility. Data about medication exposure were derived from the prescription drug claims of the health plan. Data about laboratory test completion were derived from the multispecialty group practice electronic medical record.

Determination of High-Risk Drugs and Laboratory Monitoring Guidelines

Laboratory monitoring guidelines for high-risk drugs were developed using a sequential process adapted from an approach created and tested for a study

conducted within the Health Maintenance Organization Research Network Center for Education and Research on Therapeutics. This approach used an advisory committee of national experts and local health plan leaders, including practicing clinicians and pharmacists and experts in patient safety and geriatric pharmacotherapy. The charge for this group was as follows: (1) to review a comprehensive initial list of medications requiring monitoring; (2) to assess the importance of including monitoring recommendations to evaluate efficacy, safety, and clinically relevant drug—drug interactions; and (3) to determine the need to include infrequently prescribed medications in the guidelines. The initial list of high-risk drugs included those commonly implicated in adverse events among patients in the ambulatory setting

 and those associated with adverse events leading to emergency department visits,

 as well as drugs with low monitoring rates,

 drugs included in national quality guidelines,

We asked panel members to participate in an Internet-based questionnaire administered in a 2-round modified Delphi process

 between August and October 2008. Panelists were asked whether electronic monitoring alerts should be sent to primary care physicians, specialists, or both and whether monitoring alerts should be generated for infrequently dispensed or effectively obsolete medications. Panelists were also asked to rate the importance of monitoring each candidate medication for efficacy, toxic effects, and drug—drug interactions. Each question was answered based on a 5-point Likert-type scale to evaluate agreement or disagreement with statements concerning the importance of monitoring each medication or medication class for the domain assessed. The scale ranged from 1 (indicating “strongly agree”) to 5 (indicating “strongly disagree”).

After the first round of the survey, we eliminated questions for which there was agreement and readministered questions for which there was lack of consensus. Consistent with other modified Delphi methods, 

 for a question was defined by agreement on categorization by at least a majority (>50.0%) of respondents.

 We then administered a second questionnaire to participants. In this round, panelists were reminded of their original responses to individual questions and were given the group’s aggregated response to the questions in the first round. At this stage, each participant was given the opportunity to revise his or her response to increase consensus with the succeeding round. The results of the Delphi process informed the selection of the final high-risk drug list.

Determination of Laboratory Test Monitoring Frequency

After selection of the final high-risk drug list, 2 research pharmacists (JLD and AOK) reviewed the literature to determine the appropriate frequency of laboratory monitoring for each drug. The review included monitoring recommendations provided in manufacturer labeling information, nationally available published guidelines, and clinical guidelines from national organizations and initiatives (eg, American Heart Association guidelines

 and National Committee for Quality Assurance—developed measures and guidelines

). Because manufacturers’ warnings often describe a nonspecific frequency for monitoring such as “periodically,” we reviewed other authoritative sources that pharmacists and clinicians commonly use to guide decisions about the frequency of monitoring. These sources included the following: (1) the published literature; (2) the Micromedex Healthcare Series

 (Thomson Reuters [Healthcare] Inc, Greenwood Village, CO) database, which sources the primary case report and experimental literature; (3) UpToDate Inc

 (Waltham, MA), an online peer-reviewed reference database; and (4) the Pharmacist’s Letter

 (Therapeutic Research Center, Stockton, CA). The final list of laboratory monitoring tests and the associated monitoring frequencies were reviewed by a local panel of academic pharmacists, the clinical pharmacist for the multispecialty group practice, and the medical director of the multispecialty group practice to determine local acceptability and congruence with local quality standards.

We used drug dispensing claims between January 1, 2007, and July 31, 2008, to identify the first dispensing of a high-risk medication for a patient after January 1, 2008. 

 was defined as an initial dispensing on or after the index date of January 1, 2008, and no drug dispensing in the 6 months preceding the index date. 

 was defined as a dispensing on or after January 1, 2008, with evidence of drug dispensing in the 6 months before that date. Because clinicians might rely on laboratory test results for up to 6 months before initiation of a drug or might order a test within 2 weeks after initiation of a drug, we defined 

test completion for a new dispensing in 2008

 as having occurred if there was at least 1 associated monitoring test completed 180 days before dispensing to 42 days after dispensing (test ordered 

14 days after dispensing plus 28 days for test completion). Completion of a test of serum drug levels (eg, serum carbamazepine) for new prescriptions was measured from the day of dispensing to 30 days after initial dispensing. 

Test completion for chronic medication use

 was defined as having occurred if there was at least 1 recommended test for the drug test pair that occurred up to 365 days before the index dispensing in 2008 through 42 days after the dispensing if the test was indicated annually (or 180 days before to 42 days after index dispensing if the test was indicated every 6 months). For each drug—laboratory test combination, the proportion of completed recommended tests was determined for all index dispensings in the observation period. We used a nonparametric test for trend across ordered groups by Cuzick17 to examine whether more frequently dispensed drugs had a higher prevalence of recommended test completion. All analyses were conducted using commercially available software (SAS 9.2; SAS Institute Inc, Cary, NC). The study was approved by the institutional review boards of the University of Massachusetts Medical School, Worcester, and the participating group practice.

A panel of 3 pharmacists and 6 physicians participated in the consensus survey. All physicians were certified by the American Board of Internal Medicine, 2 had an added qualification in geriatric medicine, and 3 had subspecialty board certifications. All respondents completed the 2 rounds of the survey. After the second round of the survey, consensus was achieved for all 40 medications with respect to parameters (ie, efficacy or toxic effects) for laboratory monitoring and for alerts to physicians (ie, primary care physician vs specialist). The consensus panel agreed that alerts for high-risk medications were indicated to monitor efficacy and toxic effects alone or in the presence of significant drug—drug interactions. The panel also agreed that alerts should be sent to specialists and primary care physicians and should be developed for medications that were frequently and infrequently prescribed. Because strict national prescribing policies already exist to guide the use and monitoring of isotretinoin, we did not include this medication in the newly developed guidelines. The resulting list of drugs included 39 drugs or drug classes.

Pilot Testing of Monitoring Recommendations

Once the medication list was established, we examined overall dispensings of the selected medications. The chronic index dispensings ranged from 0 (procainamide and ganciclovir)to 13,351 (statins), and new index dispensings ranged from 0 (procainamide and ganciclovir) to 2463 (statins) during the observation period of January 1, 2008, to July 31, 2008. Based on these results, 4 drugs with fewer than 5 new or chronic users combined (ticlopidine, tolcapone, ganciclovir, and procainamide) were eliminated from the final drug list, resulting in 35 drugs or drug classes in the final list of monitoring guidelines. There were 61 separate drug—laboratory test combinations for monitoring, with 15 requiring more than 1 laboratory test (eg, amiodarone–thyroid-stimulating hormone [TSH] and amiodarone–aspartate aminotransferase [AST]) and 20 requiring a single test; for the purpose of this study, we considered a basic metabolic panel that includes sodium, potassium, chloride, bicarbonate, blood urea nitrogen, and creatinine levels as a single test.

Overall, the rates of test completion ranged from 0.0% for aspartate aminotransferase or alanine aminotransferase [ALT] among new users of nefazodone, phenobarbital, and quinidine at baseline to 96.8% for AST or ALT test among chronic users of niacin (

).Drug—laboratory test pair completion differed between new and chronic medication dispensings. For new medication dispensings, less than 20.0% of the recommended 61 drug–laboratory test pairs were monitored in the majority (>75.0%) of patients. In contrast, close to 40.0% of the recommended drug–laboratory test pairs for chronic dispensings were monitored in greater than 75.0% of patients.

For drugs with multiple laboratory tests, the percentage test completion often varied within the drug (Table 2). For example, for new amiodarone use, 52.7% had an AST or ALT  test, while 74.6% had a TSH test; for azathioprine use,28.0% had a baseline AST or ALT test, while 72.0% had a complete blood count (CBC). We also found that the frequency of medication use was associated with the frequency of recommended test completion (

). Infrequently dispensed medications had a lower prevalence of test completion. Nonparametric test for trend showed a significant trend toward more frequently dispensed medications having a higher prevalence of appropriate testing (

Herein, we describe the development of drug—laboratory test monitoring guidelines based on manufacturers’ recommendations and published guidelines. Recently published guidelines to monitor drugs in the long-term care setting included a similar list of medications and laboratory tests.

 To determine the potential effect of our guidelines on actual practice, we determined the use frequency of the guideline drugs and the prevalence of testing for each of the recommended laboratory tests. We found that (1) certain high-risk drugs had very low rates of use among our target population, (2) rates of testing for high-risk drugs varied widely, and (3) infrequently used drugs had lower testing rates. Our study adds to the literature by identifying ways to inexpensively improve patient safety and quality of care (describing a method for formulating monitoring recommendations) and by providing final monitoring recommendations (detailed guidelines are available from the authors) that are useful to practitioners considering similar interventions at their local institutions.

It is important to point out that the level of evidence for each recommended drug test pair varied and that this likely explains some of the variation in testing among our sample. For example, there is increasing controversy about the role of evaluating renal function in patients taking metformin.

Among our sample, we found that 45.0% to 60.0% of metformin users had completed a serum creatinine test. Similarly, there is a difference in the role of digoxin-level monitoring depending on indication, with digoxin serum drug levels of less importance when prescribed for heart rate control and of greater importance when used for heart failure.

 Among our sample, we found digoxin levels completed for 30.0% to 60.0% of users. We did not discern the indication for digoxin use in our study, but previous investigations have shown similarly low rates of digoxin-level testing.

 Variance in the level of evidence for testing may explain some of the differences we observed in the prevalence of completion among different tests within the same drug.

We are concerned about the low prevalence of testing for drug test pair recommendations that are much less controversial such as evaluation of thyroid function among amiodarone users. Amiodarone-induced thyroid dysfunction, including thyrotoxicosis and hypothyroidism, can occur in up to 20% of users.22 For this drug test pair, we found that 75.0% of new users and 50.0% of chronic users had completed a TSH test. The reason for the lower rate of testing among new drug users is unclear. Although a possible explanation may be the initiation of drugs in the hospital, as may be the case for amiodarone among using patients with arrhythmias, the prevalence of hospitalizations did not differ among those with versus without appropriate monitoring of amiodarone in a prior study.

 Further investigation is necessary to determine whether hospitalizations explain low testing rates among our study population.

An important finding of our study was that patients using infrequently prescribed drugs were less likely to complete recommended laboratory tests. This is consistent with literature showing an association between patient volume and quality of care

 but is not supported by at least 1 study

 examining patient volume in association with process measures such as laboratory testing in diabetes. The rationale supported by the human factors model of medical errors is that physicians will likely have better familiarity of the literature for drugs that they prescribe more frequently than for drugs that they do not prescribe.

The implication of this finding can be considered in terms of potential relative and absolute effects of an intervention to improve monitoring. There is potential for significant effect from interventions aimed at improving infrequently used medications because baseline rates of testing are low. This is important for drugs such as phenobarbital, whose laboratory monitoring is a Healthcare Effectiveness Data and Information Set measurement for assessing health plan quality of care.

 In contrast, the number of patients affected by infrequently prescribed drugs is small from a population health perspective. For example, the number of patients who failed to receive monitoring of statins was 2136, more than 20 times greater than the number of patients affected by failure to monitor the 7 infrequently prescribed drugs combined (ie, imidazole antifungals, terbinafine, angiotensin II receptor blockers, isoniazid, rifampin, theophylline,and nefazodone). Health plans considering the implementation of interventions to improve medication monitoring need to weigh the absolute and relative potential effects of their program. Although the marginal cost of targeting many medications in a health information technology—based intervention to improve monitoring is minimal and allows for the inclusion of many medications, programs using high-cost resources such as pharmacist time need to limit target medications by weighing the relative overall effect carefully to optimally select target medications.

Other important drugs for inclusion in monitoring guidelines are those with a high likelihood of a serious adverse outcome. Our guidelines incorporate several drugs commonly implicated in adverse drug events leading to emergency department visits,

 including hypoglycemics, warfarin, anticonvulsants, digoxin, theophylline, and lithium. Of these, we found high rates for warfarin, which is often monitored through special anticoagulation programs, but low-to-moderate rates for digoxin, anticonvulsants, lithium, and theophylline.

Limitations of our study should be noted. First, our study was conducted in a single multispecialty group practice. Second, laboratory tests may have been ordered for another reason (ie, not for high-risk medication monitoring), so that we may have overestimated the prevalence of recommended testing. Third, we were unable to confirm patient adherence to drugs and were unable to identify patients who did not complete tests because they were no longer using the medication. Fourth, in many cases, despite the panel’s conclusions, limited evidence was found to support testing frequency or importance, and that may explain the low prevalence of test ordering for some drugs. For many drugs, further study is necessary to determine whether laboratory test monitoring improves health outcomes. Furthermore, lower testing for new use may also be explained by drug initiation in the hospital setting for drugs such as amiodarone and antiepileptics. In these cases, it is possible that baseline testing was obtained after initiation in the hospital and was not repeated after discharge to the ambulatory setting. However, similarly low rates of TSH and aminotransferase testing have been reported for chronic users of amiodarone

 and for determining serum levels of antiepileptics.

 Fifth, we did not assess all guideline recommendations such as monitoring after dose changes, drug—drug interactions, changing renal function, and clinical status.

Our study updates the laboratory monitoring recommendations for high-risk drugs to include drugs used in ambulatory practice until 2008. Our findings and guidelines are generalizable to other institutions seeking to improve monitoring of high-risk medications to enhance the safety for their patient population after review and adaptation by local medical and pharmacy leadership to ensure consistency with local standards. Health plans considering similar interventions can use our findings to weigh the potential benefit of targeting drugs of high-frequency and low-frequency use in clinical practice, as our study demonstrates potential for improvement in the laboratory monitoring of drug types. Further research is necessary to understand factors contributing to the low monitoring of high-risk drugs, including physician factors resulting in lack of test ordering and patient factors leading to incomplete performance of tests.

We acknowledge the contributions of the national expert advisory panel, including Susan Andrade, ScD; Steven Simon, MD; Jerry H. Gurwitz, MD; and Marsha A. Raebel, PharmD. Local experts included Josie Cambia-Kiely, PharmD; Mojgan Hajji, PharmD; Michael Kelleher, MD; Leslie Harrold, MD; Sarah McGee, MD; and Robert Yood, MD.

From the Division of Geriatric Medicine (JT, TSF, SJG, SHF, JHG), University of Massachusetts, Worcester, MA; Meyers Primary Care Institute (JT, TSF, YZ, JCF, JHG), Worcester, MA; Fallon Clinic (LDG), Worcester, MA; Massachusetts College of Pharmacy (JLD, AOK), Worcester, MA; and Kaiser Permanente Colorado (MAR), Denver, CO.

Pilot testing of guidelines for the laboratory monitoring of high-risk medications shows that monitoring is highly variable and that there is room for improvement.


Shawn J. Gagne, BA

Articles

Quality Measurement of Medication Monitoring in the Meaningful Use Era

Development and Pilot Testing of Guidelines to Monitor High-Risk Medications in the Ambulatory Setting