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DNP 805 A CPOE and/or CDSS Technologies Support Care Decisions

DNP 805 A CPOE and/or CDSS Technologies Support Care Decisions

DNP 805 A CPOE and/or CDSS Technologies Support Care Decisions

DNP 805 A CPOE and/or CDSS Technologies Support Care Decisions

Select a particular medication or clinical problem. Describe how the CPOE and/or CDSS technologies support care decisions in this area.

While medications can improve patients’ health, the process of prescribing them is complex and error prone, and medication errors cause many preventable injuries. Computer provider order entry (CPOE) with clinical decision support (CDS), can improve patient safety and lower medication-related costs.

To realize the medication-related benefits of CDS within CPOE, one must overcome significant challenges. Healthcare organizations implementing CPOE must understand what classes of CDS their CPOE systems can support, assure that clinical knowledge underlying their CDS systems is reasonable, and appropriately represent electronic patient data. These issues often influence to what extent an institution will succeed with its CPOE implementation and achieve its desired goals.

Medication-related decision support is probably best introduced into healthcare organizations in two stages, basic and advanced. Basic decision support includes drug-allergy checking, basic dosing guidance, formulary decision support, duplicate therapy checking, and drug–drug interaction checking. Advanced decision support includes dosing support for renal insufficiency and geriatric patients, guidance for medication-related laboratory testing, drug-pregnancy checking, and drug–disease contraindication checking. In this paper, the authors outline some of the challenges associated with both basic and advanced decision support and discuss how those challenges might be addressed. The authors conclude with summary recommendations for delivering effective medication-related clinical decision support addressed to healthcare organizations, application and knowledge base vendors, policy makers, and researchers.

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DNP 805 A CPOE and/or CDSS Technologies Support Care DecisionsIntroduction
Medications are powerful and commonly used modern therapies that can yield many benefits. Yet, they can also cause considerable harm, 1,2,3,4 especially if prescribing clinicians fail to consider relevant patient characteristics. For example, renal insufficiency and advanced patient age call for lower than usual medication doses, and drug–drug interactions are sometimes lethal. Electronic health record (EHR) systems can improve the reliability, quality, and safety of medication use. 5,6

Computerized provider order entry (CPOE) with clinical decision support (CDS) can improve medication safety and reduce medication-related expenditures because it introduces automation at the time of ordering, a key process in health care. Electronic order communication can occur instantly, accurately, and reliably and computer-generated orders are more legible than those written by hand. A knowledge-based CDS review can assure that the order is safe and compliant with guidelines. 7,8 For CDS to be effective, adequate expertise must go into defining and representing medical knowledge. Also, data that are critical for CDS, such as the patient’s weight and allergy status, must be captured and made available to the CDS system. CDS systems must support, rather than impede, clinical workflows through speedy, available, and usable algorithms that provide parsimonious, clear, concise, and actionable warnings and advice. 8,9

To help understand the state of the art of the capability of CDS to improve medication safety, efficiency, and health care quality, the authors convened a CPOE conference in San Francisco in June of 2005. Participants reviewed the common categories of medication-related CDS within CPOE. For each category of CDS, we considered: How does it work? What is the potential benefit? What (if any) are the results of studies that have documented the benefits and/or undesirable side effects? What are outstanding issues (e.g., knowledge-base management, user interface issues) that prevent the benefit from being realized to its fullest? And what are some next steps that might help the evolution of the specific category of decision support?

This review provides a literature-based summary of the discussions. Rather than exhaustively reviewing the literature on these topics, we selected papers that reflect exemplary current practice and have direct actionable relevance to system designers working to implement these features in today’s technical environment. We also identified papers that illustrate the limitations of today’s technology and can help point the way forward for future developments in the field. For each category of decision support, we make recommendations for how the effectiveness of the feature can be optimized and we conclude the paper with summary recommendations to healthcare organizations, application and knowledge-base vendors, policy makers, and researchers for how to advance the delivery of effective medication-related clinical decision support.

We divided the CDS categories into two stages—basic and advanced. The issues associated with basic CDS are more straightforward than the advanced categories, and may represent a suitable starting point for most health care organizations. In basic CDS, we included drug-allergy checking, basic dosing guidance, formulary decision support, duplicate therapy checking, and drug–drug interaction checking. Advanced decision support includes dosing support for renal insufficiency and geriatric patients, guidance for medication-related laboratory testing, drug–disease contraindication checking, and drug–pregnancy checking.

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Categories of Basic Medication-Related Decision Support
DNP 805 A CPOE and/or CDSS Technologies Support Care DecisionsDrug-Allergy Checking
Drug-allergy checking, an important patient safety feature, 10 presents an alert when a provider orders a medication to which the patient has an electronically documented allergy. Matching a documented drug allergy and an ordered medication requires that both medications and allergies be represented with a consistent coding scheme, with medications organized in antigenically related classes. 10 Potential harm to patients can occur if allergy checking is inadequate—either by missing important alerts or by generating so many unimportant alerts that clinicians ignore even important alerts.

Drug-allergy checking is inconsistent across CPOE applications and major shortcomings plague many drug-allergy checking features. For example, some applications do not require structured, coded entry of allergens and allergic reactions. Drug-allergy checking then becomes close to impossible. Nuances of drug-allergy checking vary across applications—for example, whether the system provides cross-sensitivity checking (e.g., triggering a drug-allergy alert on a cephalosporin order when the patient is allergic to penicillin) and checking the active medication list for interactions when a provider enters a new allergy (“reverse allergy checking”). 10 Also, many applications do not distinguish between a drug allergy and a drug sensitivity, depriving the physician of important information when faced with difficult, limited therapeutic options. 10

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Excessive drug-allergy alerting in clinically irrelevant circumstances is highly prevalent and a major disruptor of clinicians’ workflows. In one study, physicians accepted less than 20% of drug-allergy alerts. 11 Another study showed that almost all overrides of alerts were clinically appropriate and that overrides rarely caused serious adverse outcomes. 12 A major reason for over-alerting is that drug prescribing knowledge bases often include drug-allergy rules that are of questionable clinical value. For example, cross-sensitivity rules in commercial medication knowledge bases tend to be overly inclusive and generate many alerts with low clinical relevance. Currently, drug-“allergy” alerts based on narcotic sensitivities comprise an especially high number of unhelpful alerts. 12 For example, an allergy alert will trigger upon ordering morphine in a patient with an “allergy” to codeine for which the documented reaction is nausea and vomiting, which is in fact a sensitivity rather than a true allergy. Also, cross-sensitivity rules that alert when furosemide is ordered on a sulfa-allergic patient generate numerous clinically insignificant alerts. 11 Local effort by institutions implementing such systems to inactivate some of these cross-sensitivities from the drug-allergy knowledge base can eliminate a large number of “nuisance” alerts. 12,13 Another technique for decreasing excessive alerts would be to suppress an alert when a provider previously has seen and overridden a drug-allergy alert for the same medication in the same patient.

A second reason for over-alerting is poor quality allergy data in the clinical database. When the coded reason for an allergy alert override is “patient tolerates this medication,” the quality of the clinical data is suspect and removal of such allergies from the patient record can be considered. In one study, removal of allergy documentation in such circumstances happened less than 20% of the time. 12 Institutions should consider strategies to improve allergy removal, while retaining a record of such “outdated” allergies. Strategies to improve allergy removal should be further explored, as well as cultural issues around clinicians’ hesitance to remove allergy data, even of dubious accuracy, from the electronic medical record.

Another factor impeding optimal drug-allergy alerting is lack of knowledge, from a human factors standpoint, about the best way to present specific types of alerts to providers. 14 Important questions include: how to present highly clinically significant warnings as readily identifiable and easily distinguished from other warnings. How to define “high clinical significance” alerts. Would coding the severity (e.g., anaphylaxis vs. hives) of past reactions for the patient’s allergy address the problem, or confound it with more noise? More research needs to be done to determine the most effective ways to differentiate high-severity alerts.

DNP 805 A CPOE and/or CDSS Technologies Support Care DecisionsResearch suggests that medication CDS should include a number of specific allergy-related features. 10,12 These include:

1 All clinicians who customarily document allergies should be allowed to contribute to a common allergy database.10 Organizations that cannot provide a single allergy database should link multiple ones.15

2 Allergy documentation should require a coded allergen and coded reaction (discriminating between true allergies and sensitivities/intolerances).

3 Drug-allergy checking should include evidence-based cross-sensitivity checking (clearly indicating, and possibly not alerting, if reported cross-sensitivities occur rarely).

4 Applications should include “reverse allergy checking” (defined above).

5 Knowledge developers should avoid over-alerting by improving specificity of alerts and by improving allergy data quality. Removal of an allergy from the allergy list should be facilitated when a physician overrides a drug-allergy alert.

6 Analyses of override reasons should occur as part of system quality improvement efforts, and contribute to further reduction of non-essential alerts.

7 Once over-alerting is under control, systems should ask the clinician to provide a coded override reason whenever he or she overrides drug-allergy alert. The override reason should allow nurses and pharmacists to understand the rationale for the override.

8 Research should continue into how to make drug-allergy alerting as effective as possible. The way alerts are presented to providers should be improved in part through differential display based on the severity of the anticipated event.

DNP 805 A CPOE and/or CDSS Technologies Support Care DecisionsBasic Dosing Guidance for Medications in CPOE
In non-automated and some automated ordering environments, dosing mistakes comprise the most common type of medication error leading to preventable adverse drug events (ADEs). 3,16–19 In one inpatient study, over 60% of prescribing errors involved wrong medication doses or improper administration frequencies. 20 Susceptible patient populations, particularly pediatric and geriatric age groups, are at risk of serious dosing errors, especially over-dosing. 2,21–24 Pathophysiological conditions and comorbidities, such as renal insufficiency, may further complicate the patient’s medication dosing requirements, putting him or her at increased risk for preventable injury. Medications with low margins of safety, such as nephrotoxic antibiotics, oncologic agents, sedatives, and narcotics create opportunities for serious dosing errors.

DNP 805 A CPOE and/or CDSS Technologies Support Care DecisionsCPOE with CDS can improve medication dosing through multiple mechanisms. A simple, minimally intrusive method is to offer the clinician a list of patient-appropriate dosing parameters for each specific medication, and to facilitate through defaults the selection of the most appropriate initial dose. 25 This can dramatically decrease variability in initial dosing. 25 Another approach to CPOE dosing enhancement includes provision of lists of complete order sentences, defined as “complete pre-written medication orders that include dose, dose form (when necessary), route of administration, frequency, and a PRN flag and reason (if necessary)” (see ▶). Alternatively, the system may provide separate recommendations for dose and frequency. 7 Choosing from pre-defined lists decreases errors due to a mental “slip,” a misplaced decimal point, or using the wrong dosing unit (e.g., grams instead of milligrams). 17,19 One study determined that pre-defined order sentences might prevent over 75% of 1,111 dosing errors. 20 Another study of outpatient prescribing determined that default dose and frequency suggestions might have eliminated 42% of prescribing errors and 53% of potential adverse drug events. 19 Providing constrained lists of dosing options both delivers dosing guidance and improves user acceptance of the system by enhancing workflow. 8