The medication minefield: using computerized decision support systems to reduce preventable adverse drug events and hospitalizations.

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J Ambulatory Care Manage Vol. 37, No. 3, pp. 226–240 C 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins Copyright

The Medication Minefield Using Computerized Decision Support Systems to Reduce Preventable Adverse Drug Events and Hospitalizations Richard Bernstein, MD; Polina Kogan, PharmD; Arlen Collins, MD Abstract: Preventable adverse drug events (ADEs) are a source of avoidable hospitalizations, morbidity and mortality, especially among those older than 65 years. Computerized decision support systems (CDSSs) can identify and address ADEs, but relatively little has been written about the effectiveness of such system in the community setting. This article will review some important studies on the causes of medication-related admissions in the ambulatory setting, where a lack of communication among prescribers creates a virtual minefield of medication risk. Some preliminary data will show how the application of CDSSs can affect the outcomes of care, including a reduction in preventable admissions and readmissions. Key words: adverse drug events, decision support systems, drug toxicities, hospital utilization, iatrogenic disease, medication error, pharmacovigilance, preventable admissions, risk factors

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EDICATIONS REPRESENT the most common intervention in health care . . . [They] also lead to an estimated 1.5 million adverse events and tens of thousands of hospital admissions each year” (Classen et al., 2011). The issue of medication-related complications and safety received broad attention following the publication of the Institute of Medicine’s To Err Is Human: Building a Safer Health System in 1999 (Kohn

Author Affiliations: Icahn School of Medicine at Mount Sinai, New York, New York (Dr Bernstein). Visiting Nurse Service of New York, New York (Dr. Kogan). REMEDIA, LLC, Amherst, Massachusetts (Dr. Collins). Besides Dr. Collins, who is President and Chief Science Officer of REMEDIA, the authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Correspondence: Richard Bernstein, MD, Icahn School of Medicine at Mount Sinai, New York, NY 10029 ([email protected]). DOI: 10.1097/JAC.0000000000000033

et al., 1999). This landmark publication and earlier work documented the contribution of medication errors to hospital complications and cost. These studies did not focus on the relationship of adverse drug events (ADEs) in the ambulatory setting to admissions and readmissions. This article will use the term adverse drug event similar to one used by the Agency for Health Research and Quality (AHRQ) and its Patient Safety Network (PSNet), namely, an adverse event involving the use of medication, some of which are preventable or ameliorable. In the latter instance, while not preventable, the consequences and complications could be mitigated (PSNet, n.d.). Since 1955, researchers have noted that a small subset of individuals with multiple admissions for chronic conditions had a disproportionate impact on health care costs and readmissions (Roth et al., 1955). Later studies showed it was repeated admissions for the same condition along with unexpected complications that characterized the group

226 Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

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The Medication Minefield of high-cost patients (Zook & Moore, 1980). This group was noted to often be elderly and to have multiple comorbidities. Other studies in the mid-1980s, based on databases used by professional review organizations, began to analyze the demography, cost and patterns of repeated hospitalizations, because the federal government was concerned about the cost burden of such readmissions (Anderson & Steinberg, 1985a, 1985b; Gooding & Jette, 1985; Fethke et al., 1986; Smith et al., 1985). In 1991, Reed pointed out that studies from the professional review organizations recognized that their administrative and demographic information could not necessarily define causes as much as correlations between age, prior admissions, and other features of patients readmitted to hospital (Reed et al., 1991). His study was based on Veterans Administration hospital record abstraction and found that, besides prior admissions, medication dosage change within 48 hours prior to discharge was one factor for readmission risk. A number of subsequent studies from 1990s to the present, both in the United States and around the world, document the prevalence of drug-related admissions, the drugs most commonly associated with admissions, and other associated risk factors. A representative sample of 20 of such studies is summarized in the Appendix and indicates that ADEs occur in about 15% of those older than 65 years in institutional setting. A few studies show similar findings in the community settings. About half the adverse events were judged to be preventable. These events commonly involved falls, orthostatic hypotension, heart failure, mental status changes, renal failure, and gastrointestinal bleeding. Preventable admissions were most often related to antidiabetic and antithrombotic medications, diuretics, and nonsteroidal antiinflammatories (Pretorius et al., 2013), and drugs that are potentially inappropriate in the elderly population (American Geriatrics Society 2012 Beers Criteria Update Expert Panel, 2012). Recognizing that potentially preventable admissions and complications are related to

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ADEs is an important step, mitigating the observed risks remains a significant health system challenge. CLINICAL DECISION SUPPORT SYSTEMS Observations from the summarized international studies indicate a variety of errors of commission and omission. Risk factors noted include advanced age, interprofessional noncommunication or miscommunication, patient education, and behavioral lapses, such as self-administration errors. While some of these factors cannot be influenced or observed by pharmacovigilance (WHO, 2014), actionable steps can nonetheless be taken to design a decision support and alerting system to address a broad spectrum of causes of ADEs that may cause preventable complications and admissions. An ideal system would address most of the items included in Figure 1, many of which were identified by the World Health Organization Media Centre fact sheet on medication safety and adverse drug reactions (WHO Media Centre, 2008). The broad spectrum of elements in an ideal system can be divided into 5 categories: 1. STOP: Discontinue medications due to drug-drug interactions (DDIs) or to avoid medications not recommended based on age, sensitivities, exhibited symptoms, or known diseases and conditions 2. MODIFY: Change medication doses to avoid over- and underdosing 3. MONITOR: Perform drug and laboratory monitoring of therapy to detect risk of complications from under- and overdosing, potentiation effects, etc. 4. START: Initiate medication to address errors of omission based on evidence-based treatment guidelines 5. COUNSEL: Provide education and coaching directed at patients, prescribers, and/or pharmacy staff about specific safety issue The grading of evidence supporting all recommendations regarding DDIs, dosing issues, and treatment protocols should be included in computerized decision support systems (CDSSs) to help clinicians balance the

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JOURNAL OF AMBULATORY CARE MANAGEMENT/JULY–SEPTEMBER 2014

1. STOP (errors of commission) a. Medication potentially inappropriate for the patient’s age, renal and/or liver function, e.g., Beers criteria updated by the American Geriatric Society (2012) b. Potentially serious drug-condition (including pregnancy) and drug-disease interactions c. Drug duplication within the same class d. Potential abuse risk and drug diversion, especially with multiple prescribers and multiple pharmacies e. Administering medication to which the patient has a known intolerance or allergy 2. MODIFY (errors of commission) a. Medication dosage unsafe for the patient’s age, renal, and/or liver function relative to age b. Underdosing of medication compared with evidence-based guidelines c. Use of medication for which there is a safer alternative for the same condition d. Inadvertent drug overdosage related to multiple prescribers unaware of other prescribers’ actions e. Polypharmacy with opportunities for simplifying drug regimens 3. MONITOR (errors of omission) a. Lack of drug level (e g, digoxin, anticonvulsants) and laboratory monitoring (e g, potassium, liver and renal function tests, international normalization ratio [INR]) 4. START (errors of omission) a. Not prescribing medication when appropriate for those conditions with strong evidence-based therapy recommendations 5. COUNSEL a. Nonadherence to chronic medications; exploring adherence barriers (cost, access, adverse effects, etc.) b. Drug-diet interactions that adversely affect therapeutic drug levels c. Identifying multiple prescribers with inadvertent DDI and dosing-related medication risks d. Inappropriate self-medication or misunderstanding about how to take particular medication e. Use of a sub-standard or counterfeit medication f. Dispensing errors (pharmacist-directed counseling)

Figure 1. Elements of an ideal medication clinical decision support system.

benefits and risks of point of care decision making. Differentiating between recommended and mandatory “STOP” alerts should be included. In addition, the ideal system should be able to quickly generate reports that summarize data at the population, practice site, facility, and prescriber level as well as drill down to the individual patient level so that corrective interventions can be taken. Finally, push alerts should be able to inform prescribers, dispensers, care managers, and clinical pharmacists and possibly patients about medication safety issues detected to prevent or to at least make timely corrective actions regarding identified medication risks. Beers developed the first widely used screening tool to identify medication errors in 1991 (Beers et al., 1991). It was initially developed using a Delphi technique among experts in therapeutics to identify inappropriate prescribing in a nursing home setting. It has gone through several subsequent updates and now applies to facility and community settings to

help practicing physicians avoid prescribing potentially inappropriate medications in the elderly population. It was designed to be easily applied to e-prescribing formats in computerized provider order entry systems (CPOEs) within institutions or in stand-alone electronic health records (EHRs). In 2007, a screening tool called STOPP/ START was another important effort to help prescribers prevent ADEs from both errors of commission and errors of omission. The latter were not part of the original and updated versions of the Beers criteria. STOPP (screening tool of older person’s prescriptions) and START (screening tool to alert doctors to right treatment) (Gallagher et al., 2008) also evolved by employing a Delphi consensus technique to validate the content among a panel of experts from academic centers in Ireland and the United Kingdom. The tool consists of 65 STOPP criteria that describe inappropriate prescribing in the elderly people and their rationale; START includes 22 evidence-based medication regimens for


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The Medication Minefield the medication management of common conditions in older individuals. STOPP was shown in many studies to be superior to Beers’ criteria in preventing ADEs and to overcome many of Beers’ recognized limitations (Curtain et al., 2013; Hamilton et al., 2011; Lam & Cheung, 2012; OMahony et al., 2010; Page et al., 2010). CDSSs TO MITIGATE ADEs Tools like Beers and STOPP/START and less common alternatives, such as the Medication Appropriateness Index (Hanlon et al., 1992; Samsa et al., 1994) and Improved Prescribing in the Elderly Tool (Naugler et al., 2000) are lists of explicit criteria. To be most effective and consistently implemented, they need a technical implementation to impact preventable medication errors. Furthermore, the complexity of drug-drug interactions makes the format of criteria lists particularly impractical without software alerts “pushed” to the prescriber and/or dispenser. While the use of wireless handheld devices to prevent ADEs would seem to be a feasible and logical way to bring decision support to prescribers at the point of making prescribing decisions, a recent study showed this approach was not successful in reducing clinically important drug-drug interactions (Malone & Saverno, 2012). Many medical centers have expanded CPOEs to their associated outpatient clinics and affiliated practices. These e-prescribing systems are integrated into EHRs and usually include a commercial or institutionally developed set of algorithms for detecting drug-drug interactions, allergy alerts, and guideline-driven alerts to preventive and chronic disease management recommendations (Tiwari et al., 2013). Computer analysis of EHR data can also help identify incidents of ADEs (Honigman et al., 2001). Because there is no general agreement about the optimal set of drug-drug interactions, nor consensus about which can be overridden or other decision alert rules, each CPOE and commercial EHR has unique features and variable performance, especially

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in detecting DDIs (Classen et al., 2011; Phansalkar et al., 2011). In addition, there is a risk that the signal:noise ratio will result in alert fatigue and that prescribers will ignore or bypass many of the system’s safety alerts (Smithburger et al., 2011). Nonetheless, within a facility or an integrated delivery system, their performance in reducing preventable ADEs is well documented (Bates et al., 1998), although CPOEs may not reduce all types of ADEs and the results may vary by vendor (Leung et al., 2012). To minimize alert fatigue, warning and recommendations should be prioritized and rated on their relative importance (Jung et al., 2013; Riedmann et al., 2011). Despite the long history of CPOE development, significant challenges remain and have been well documented (Kuperman et al., 2007). Proposed steps to address needed changes in e-prescribing systems have been outlined by the Joint Clinical Decision Support Workgroup and endorsed by the American Medical Informatics Association (Miller et al., 2005). Others have also proposed recommendations to CDSS adoption across various health care settings (Phansalkar et al., 2011). There is some evidence that current CDSS, which alert providers about chronic disease interventions (errors of omission) are disappointing in terms of their impact on the outcomes of care, such as admissions and disease exacerbations (Roshanov et al., 2011). Surprisingly, one study found that outpatient e-prescribing systems may produce a 10% error rate, which is similar to the rate with hand-written prescribing, suggesting that there is an urgent need for improvement in most systems (Nanji et al., 2011). THE REALM OF COMMUNITY-BASED PRESCRIBING: AN INTERPROFESSIONAL TOWER OF BABEL There are many details and challenges to be addressed for hospital-centered and hospitalbased integrated delivery systems and their CPOEs and CDSS. Nonetheless, it is important that the majority of prescriptions are written by clinicians in independent practices whose


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EHRs, if they have them at all, do not share information with the variety clinicians and settings where other professionals are also prescribing for their patients. In the community setting, the use of e-prescribing versus manual prescription writing does not show a significant impact on the prevalence of ADEs (Gandhi et al., 2003). This fragmentation, lack of interoperable e-prescribing systems and lack of interprofessional communication create a virtual Tower of Babel and represent a great risk of ADEs for the majority of the population. It is particularly a problem for the most complex and costly seniors, who are often on 6 or more drugs a day and who suffer with multimorbidity (Field et al., 2004; Kberlein et al., 2013). Those at most risk would be the 10% of Medicare beneficiaries who consume about 75% percent of Medicare expenditures (Garber et al., 1997); 1% of high-risk individuals can account for 27% of total expenditures in a geriatric setting (Bodenheimer & Berry-Millett, 2009). In addition, 8% of Medicaid enrollees account for roughly two-thirds of all Medicaid spending (Sommers & Cohen, 2006). These high-cost patients frequently have multiple hospital stays, and a large percent are described as ambulatory care sensitive. The prevalence of potentially inappropriate prescribing among elderly people (>70 years old) among community-dwelling individuals was found to be 42%, with a measurable impact on accidents and emergencies, admissions, and quality of life (Cahir et al., 2014). In 1999, “Medicare beneficiaries with 4 or more chronic conditions were 99 times more likely than a beneficiary without any chronic conditions to have an admission for an ambulatory care sensitive condition . . . . Per capita Medicare expenditures increased . . . from $211 among beneficiaries without a chronic condition to $13,973 among beneficiaries with 4 or more types of chronic conditions” (Wolff et al., 2002). Community-dwelling seniors have a high rate of being prescribed inappropriate medications (Hanlon et al., 2002) with rates from 12% to more than 21% (Gallagher et al., 2007;

Liu & Christensen, 2002). In addition, the number of different medications was found to be predictive of ADE. In a prospective cohort study, each additional medication produced a 10% increase in the mean number of events per patient per year. The prevalence of ADEs was 25%, and serious ADEs were found in 13% of the cohort; 39% of all ADEs were preventable or ameliorable (Gandhi et al., 2003). The more serious ADEs have a higher rate of preventability (Gurwitz et al., 2003), and many of these result in hospitalizations (Thomsen et al., 2007). High-risk seniors are often cared for by multiple clinicians across the continuum of their care. For example, Medicare beneficiaries on average are treated by 6 to 7 unique physicians annually (Berenson & Horvath, 2002; Pham et al., 2007). The potential impact of multiple prescribers in multiple settings without an integrated medication decision support system to help them safely prescribe for patients with multiple chronic conditions who take multiple drugs is the basis for a medication minefield of risk including preventable complications, admissions, and even death. ADDRESSING THE CHALLENGE OF THE MEDICATION MINEFIELD Although integrated health systems would seem to be an ideal environment to minimize preventable ADEs, even in such environments not all prescription data are captured. The panorama of prescriptions written for an individual is even more circumscribed when viewed in an individual or group’s EHR, because emergency department, hospital, and outside prescribers’ medications would not be included. One way of addressing this challenge is to use daily inputs from pharmacy claims to provide a near complete universe of medications dispensed for all providers on behalf of nonhospitalized patients. This could allow pointof-prescribing alerts to all providers who use e-prescribing systems and to any clinician with portal access to data from a pharmacy benefit manager (PBM). Unfortunately, with a variety of insurers using different PBMs, the


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The Medication Minefield ideal of real time point-of-prescribing alerts is not yet practical in most community practice environments. Theoretically, while most dispensing community pharmacies have their affiliated PBMs’ data and receive alerts about known allergies, duplications, and DDIs, these capabilities may be undermined by the multiple demands on retail pharmacists and the underperformance of their pharmacy and PBM CDSS software (Saverno et al., 2011). In addressing the real-world realities described earlier, it is still possible to mount a vigorous effort to identify the minefield of risk and to mitigate the preventable errors of omission and commission. VNSNY CHOICE Health Plan, serving a high-risk population of Medicare, Medicaid, and dual eligibles in New York, has been using a commercially available CDSS system, which obtains data from the plan’s PBM within 24 hours of prescription filling events. Although all potential interventions occur after prescriptions have been ordered and filled, there is evidence that, in the community setting, this can be effective in improving outcomes (Pearson et al., 2009). In addition, the CDSS vendor (Remedia, n.d.) receives twice weekly updates of all medical claims data and monthly updates of the plan’s enrollment and provider files. The CDSS employs a variety of algorithms to identify 10 domains of risk and generate alerts, updated daily and available via a portal to all validated end users. It is used by the plan’s care managers, medical directors, and clinical pharmacists for population-wide surveillance of prescribing patterns that put individuals at risk. Alerts are prioritized to allow the clinical staff to outreach to prescribers and, at times, to patients to modify the potential risks observed. Summaries of outlier prescribing patterns for group practices are also generated. The 10 domains available include the following: 1. Drug class duplications and avoidable polypharmacy alerts 2. Drug over- and underdosing alerts 3. Drug-drug and drug-disease interaction alerts

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4. Abuse risk (eg, narcotics, sedative, barbiturates, and anxiolytics) 5. Alerts to multiple prescribers and pharmacies 6. Tracking the percent adherence to selected chronic medications (oral antidiabetics, statins, renin angiotensin system inhibitors, antiretrovirals) 7. Alerts to symptom side effects related to current medications 8. Potentially inappropriate medications for seniors 9. Drugs prescribed for which lower-cost alternatives are available (adherence risk factor) 10. Alerts about conditions for which evidence-based therapy is not being used Other CDSS are available, and the dominant sources for these are the Drug Therapy Monitoring System from Wolters Kluwer Health and the National Drug Data File Plus from First Data Bank. Their modules are usually incorporated into commercially available EHRs. The CDSS from these 2 companies include comprehensive listing and severity grading of drug-drug interactions and alerts for drug doses that may be inappropriate (Figge, 2012). One advantage of an EHR-based CDSS is the ability of the end user to include allergy alerts to supplement the DDI and drug dose alerts. Point of dispensing systems from PBM and all state Medicaid programs typically incorporate similar features into their drug utilization review programs. Health plans may integrate data from their PBM and internal enrollment and claims systems into their CDSS. Such an integrated CDSS can enable care managers and clinical pharmacists to prioritize the need to intervene on ADE risks in any or all of the 10 domains listed earlier. A scoring algorithm enables this prioritization so that, depending on staffing resources and time, efforts can focus on enrollees with the highest scores. In addition, for a particular care manager’s panel of patients, Remedia software can generate a summary of the alerts pertinent to a


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Figure 2. Summary profile at the individual patient level.

given patient (see Figure 2) including potential errors of commission and omission. As mentioned, part of the minefield is generated by the lack of integrated e-prescribing systems and EHRs. This software allows providers to access their patient’s medication profile and displays all medications prescribed for their patients along with the prescriber’s name (see Figure 3). The specialty and phone numbers of the various prescribers and dispensing pharmacies is also available in a different view of this screen. In this example, the potential for narcotic abuse or

inadvertent harm could result from the lack of communication among the physicians and pharmacies involved. Class duplications are displayed with plain language alerts for end users. Diagonal striations indicate multiple or overlapping prescriptions (see Figure 4) Drug dosing alerts are also displayed as graphs and information about maximum safe prescribing levels. Similarly, other high-risk medication alerts and drug-condition alerts explain to end users the concerns so that regimen changes can be initiated (see Figure 5).

Figure 3. Abuse risk—multiple prescribers of opioids (Note: names are fictitious).


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Figure 4. Drug class duplication.

USING THE CDSS TO MITIGATE ADMISSION RISK: INITIAL OUTCOME STUDIES VNSNY CHOICE currently employs 1 fulltime clinical pharmacist for every 4000 members. This staffing ratio allows the plan to develop interventions that involve not only errors of commission but also qualityrelated concerns of undertreatment such as nonadherence to medication, underdosing of medications for specific conditions, and recommendations for initiating medications indicated but not prescribed for certain conditions. Studies have shown that about half of all avoidable ADEs could have been prevented by pharmacists (Kelly, 2001).

Reducing potentially preventable admissions and readmissions are important targets for interventions by plan staff. The CDSS indicated to the clinical pharmacy managers that the most frequent Medicare Severity Adjusted Diagnosis Related Group (MS DRGs) for plan members was “Syncope and Collapse” (MS DRG 312). After reviewing the medication profiles of this cohort of admissions, many individuals were noted to be taking medication from the Beers’ list or STOPP list sedatives, along with DDIs and duplicative therapies that could directly contribute to this DRG. When reviewing admissions for hip and other fractures (MS DRG 536), a similar pattern was found, suggesting that many of these might be iatrogenic and subject to

Figure 5. High risk medication alerts.


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intervention. While much less common in the population, preventable gastrointestinal bleeding admissions could also be often traced to anticoagulant and/or antiplatelet drug DDIs and inappropriate dosage or monitoring-related issues. In retrospect, clinical pharmacists noted multiple prescribers or likely prescriber-patient miscommunications contributing to “anticoagulant intoxication” and the risk of severe bleeding episodes. The plan’s clinical pharmacists identify potential ADEs and outreach to alert prescribers. If the communication is after an admission, a letter informs them about the likely contributing factors. The prescriber is asked to provide a fax response backed with a follow-up plan, and if he or she does not intend to change or adjust current therapy, an explanation is requested. The clinical pharmacists track all changes in prescriptions in response to their outreach efforts to physicians. If no fax response is received or if no change is noted within 2 months, a call is placed to the prescriber to review the concern. The rate of regimen change has been 60.8% for syncope interventions related to admissions. A parallel project involved concurrent identification of 3 or more antihypertensive medications prescribed with antidementia medications or alpha-blockers and 3 or more antihypertensives. The response rate was lower but still significant (34%). Clinical pharmacist staff are also tracking admissions per thousand members for DRG 312 (syncope) and 536 (fractures). The data obtained by comparing the first 2 quarters of 2012 and 2013 indicate a reduction of

more than 25% in the admission rate. The reduction in bleeding-related DRGs is being tracked, but relatively low frequency makes meaningful reductions harder to document at present. CONCLUSIONS Adverse drug events are a major cause of preventable complications, human suffering, hospitalizations, and medical costs. Controlling ADEs within an institutional setting, such as a hospital, nursing home, or integrated delivery system, involves a well-defined group of prescribers, usually a shared CPOE with a CDSS that can be customized to meet the patterns of drug use and frequent occurrences of DDIs and other sources of ADEs. Nonetheless, challenges in even these relatively closed systems remain. The reduction in ADEs in the community setting represents a much greater task, especially for populations of seniors with concomitant chronic illnesses, on multiple medications, visiting multiple physicians whose e-prescribing systems rarely interchange information. Despite these barriers, CDSS systems can be designed to address multiple domains of ADEs and their associated errors of omission and commission. They can provide prescribers, clinical pharmacists, medical directors of health plans and medical groups, as well as nurse care managers timely alerts about risks for serious ADEs. Thoughtful and systematic interventions, informed by such systems, can have a measurable impact on reducing ADEs.

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Peyriere et al. (2003)

Howard et al. (2003)

McDonnell and Jacobs (2002) NSAIDs, antiplatelets, antiepileptics, hypoglycemics, diuretics, inhaled corticosteroids, cardiac glycosides, beta-blockers

Diuretics, calcium channel blockers, NSAIDs, digoxin

156 patients admitted to a French hospital

437 adverse drug reactions, retrospective chart review in one university hospital Pharmacist review of 4093 medical admissions

VA General medical clinic; 1 year of repeated interviewing community dwelling seniors on 5 or more medications 81 academic hospitals, 28 411 consecutive admissions

Beers et al. (1991)

17.9% and 10.2% of admissions related to renal and hepatic insufficiency, respectively; 2% of admissions related to drugs of known intolerance. 57.9% avoidable. Therapeutic errors: inappropriate administration, drug-drug interactions, dosage error, drug not stopped despite the onset of ADEs (Continues)

67% preventable errors due to prescribing, monitoring, and adherence

Age, number of comorbidities and number of medications were predisposing factors 33% patient nonadherence

“ . . . much drug-related morbidity in the elderly population may be avoidable, as it is due to inappropriate prescribing.” Unusual features: clinic and not hospital-based population of seniors on 5 or more medications

Nonadherence to thiazides and antiasthmatics were associated with preventable admissions

Other Observations

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Onder et al. (2002)

416 consecutive admissions at a British teaching hospital

1999 consecutive admissions to 6 medical wards

Method

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Lindley et al. (1992)

Antirheumatics and analgesics; cardiovascular drugs; psychotropic drugs; antidiabetic drugs, antibiotics and corticosteroids

Drug Classes Involved

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Hallas et al.(1992)

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Leendertse et al. (2008)

Alexopoulou et al. (2008)

Geriatric unit in Italy. Use of Naranjo algorithm to define likelihood of drug use and ADR; Hallas criteria to evaluate avoidability of ADR. MDs, pharmacist, and pharmacologist involved NSAIDs, diuretics, aspirin, oral Prospective study of consecutive Age and number of medications anticoagulants, oral hypoglycemics admissions over 6 months at a single correlated with ADR-related admissions hospital in Greece Risk factors for medication-related Prospective unplanned admissions Hospital Admissions Related to preventable admissions: cognitive (n = 13 000) at 21 hospitals in the Medication (HARM) Study impairment, 4 or more comorbidities, Netherlands. Case control design to dependent living situation, impaired determine risk factors for preventable renal function, nonadherence to admissions medication regimen, and polypharmacy. (Continues)

Most frequent (42.7%) error of commission in ambulatory setting causing preventable ADEs were use of inappropriate drugs. Inpatient error of omissions related to inadequate monitoring (45.4%) of diuretics, hypoglycemic, and anticoagulants.

Summary of 29 studies; 14 ambulatory based and 15 hospital based

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Digoxin, NSAIDs, oral anticoagulants, and aspirin often involved

25% prevalence of drugs inappropriate in the elderly

Other Observations

186 patients over age 60 admitted to a Brazilian teaching hospital

Literature review of adverse drug reactions (1980–2002) Prospective study of 18,820 admissions at 2 hospitals

Method

JACM-D-13-00085

Franceschi et al. (2008)

NSAIDs, diuretics, Aspirin. Interactions: aspirin + warfarin; diuretic + ACE inhibitors; digoxin and anticoagulant potentiation. Passarelli et al. (2005) Digoxin toxicity, hypokalemia from diuretics and drugs inappropriate for the elderly Thomsen et al. (2007) 86.5% of preventable ADEs were cardiovascular, analgesics and hypoglycemic agents.

Pirmohamed et al. (2004)

von Laue et al. (2003)

(Continued)

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The Medication Minefield 239


Method

Other Observations

May 19, 2014

Abbreviations: ACE, angiotensin-converting enzyme; ADE, adverse drug event; ADR, adverse drug reaction; NSAIDs, nonsteroidal anti-inflammatory drugs; WHO, World Health Organization; VA, Veterans Affairs.

Polypharmacy in elderly population was Used WHO definition of ADR. Summary a contributory factor. of 25 studies. Communication failures between “Technical solutions to preventable patients and health care professionals PDRAs [preventable drug related and between different health care admissions] will need to take account professionals, knowledge gaps about of this complexity and are unlikely to drugs be sufficient on their own.” Olivier et al. (2009) Risk factors: polypharmacy, drug-drug Elderly admitted to a French hospital interactions, self-medication, antithrombotics, antibacterial drugs. Hamilton et al. (2011) 600 patients, 65 years and older at university teaching hospital with acute admissions Leendertse & van Based on HARM study (see above) Dijk (2012) Jonikas and Mandl Adherence study of antihyperlipidemics, “Efforts to use these data [on risk for (2012) antihypertensives, and oral non-adherence] in point of care and hypoglycemics for 2 million subjects decision support facilitating patient over 1 year. [care] are warranted”


240

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Kongkaew et al. (2008) Howard et al. (2008)

(Continued)

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Ambulatory Computerized Prescribing and Preventable Adverse Drug Events.

From adverse drug event detection to prevention. A novel clinical decision support framework for medication safety.

Prevention of adverse drug events through computerized surveillance.

Evaluation of Perioperative Medication Errors and Adverse Drug Events.

The experiences of using a computerized decision support system.

Hospitalizations Due to Adverse Drug Events in the Elderly-A Retrospective Register Study.

Computerized Decision Support Improves Medication Review Effectiveness: An Experiment Evaluating the STRIP Assistant's Usability.

Polypharmacy in chronic diseases-Reduction of Inappropriate Medication and Adverse drug events in older populations by electronic Decision Support (PRIMA-eDS): study protocol for a randomized controlled trial.

Alcohol and drug testing of health professionals following preventable adverse events: a bad idea.

Improving appropriateness of acid-suppressive medication use via computerized clinical decision support.

Computerized Clinical Decision Support: Contributions from 2014.

Transition From Paper to Computerized Pharmacist Clinical Decision Support.

The support of medication reviews in hospitalised patients using a clinical decision support system.

The quest to reduce the risk of adverse medical events in exercising individuals: introducing the SAFER (Strategies to reduce Adverse medical events For the ExerciseR) studies.

Decision-Making Support Using a Standardized Script and Visual Decision Aid to Reduce Door-to-Needle Time in Stroke.

Decision time for clinical decision support systems.

Relationship of adverse events and support to RN burnout.

Benzodiazepine Use During Hospitalization: Automated Identification of Potential Medication Errors and Systematic Assessment of Preventable Adverse Events.

Efficacy of education followed by computerized provider order entry with clinician decision support to reduce red blood cell utilization.

Incidence and determinants of medication errors and adverse drug events among hospitalized children in West Ethiopia.

Development of decision support systems.

Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis.

Exploring in-hospital adverse drug events using ICD-10 codes.

Computerized decision support in adult and pediatric critical care.