Increasing the detection and response to adherence problems with cardiovascular medication in primary care through computerized drug management systems: a randomized controlled trial

Improving medication adherence in cardiovascular disease

Non-adherence to medication is a global health problem with far-reaching individual-level and population-level consequences but remains unappreciated and under-addressed in the clinical setting. With increasing comorbidity and polypharmacy as well as an ageing population, cardiovascular disease and medication non-adherence are likely to become increasingly prevalent. Multiple methods for detecting non-adherence exist but are imperfect, and, despite emerging technology, a gold standard remains elusive. Non-adherence to medication is dynamic and often has multiple causes, particularly in the context of cardiovascular disease, which tends to require lifelong medication to control symptoms and risk factors in order to prevent disease progression. In this Review, we identify the causes of medication non-adherence and summarize interventions that have been proven in randomized clinical trials to be effective in improving adherence. Practical solutions and areas for future research are also proposed.

Feasibility of implementing a web-based tool built from pharmacy claims data (e-MEDRESP) to monitor adherence to respiratory medications in primary care

OBJECTIVE

e-MEDRESP is a novel web-based tool that provides easily interpretable information on patient adherence to asthma/chronic obstructive pulmonary disease (COPD) medications, using pharmacy claims data. This study investigated the feasibility of implementing e-MEDRESP in primary care.

MATERIAL AND METHODS

In this 16-month prospective cohort study, e-MEDRESP was integrated into electronic medical records. Nineteen family physicians and 346 of their patients were enrolled. Counters embedded in the tool tracked physician use during the follow-up. Patient/physician satisfaction with e-MEDRESP was evaluated though telephone interviews and online questionnaires. The capacity of e-MEDRESP to improve adherence was explored using a pre-post analysis.

RESULTS

Overall, 245 patients had at least one medical visit during follow-up. e-MEDRESP was consulted by 15 (79%) physicians for 85 (35%) patients during clinic visits. Seventy-three patients participated in telephone interviews; 84% reported discussing their medication use with their physician; 33% viewed their e-MEDRESP report and indicated that it was easy to interpret. The physicians reported that the tool facilitated their evaluation of their patients' medication adherence (mean ± standard deviation rating: 4.8 ± 0.7, on a 5-point Likert scale). Although the pre-post analysis did not reveal improved adherence in the overall cohort, adherence improved significantly in patients whose adherence level was <80% and who were prescribed inhaled corticosteroids (26.9% [95% CI 14.3-39.3%]) or long-acting muscarinic agents (26.4% [95% CI 12.4-40.2%]).

CONCLUSIONS

e-MEDRESP was successfully integrated in clinical practice. It could serve as a useful tool to help physicians monitor their patients' medication adherence.

Clinician collaboration to improve clinical decision support: the Clickbusters initiative

OBJECTIVE

We describe the Clickbusters initiative implemented at Vanderbilt University Medical Center (VUMC), which was designed to improve safety and quality and reduce burnout through the optimization of clinical decision support (CDS) alerts.

MATERIALS AND METHODS

We developed a 10-step Clickbusting process and implemented a program that included a curriculum, CDS alert inventory, oversight process, and gamification. We carried out two 3-month rounds of the Clickbusters program at VUMC. We completed descriptive analyses of the changes made to alerts during the process, and of alert firing rates before and after the program.

RESULTS

Prior to Clickbusters, VUMC had 419 CDS alerts in production, with 488 425 firings (42 982 interruptive) each week. After 2 rounds, the Clickbusters program resulted in detailed, comprehensive reviews of 84 CDS alerts and reduced the number of weekly alert firings by more than 70 000 (15.43%). In addition to the direct improvements in CDS, the initiative also increased user engagement and involvement in CDS.

CONCLUSIONS

At VUMC, the Clickbusters program was successful in optimizing CDS alerts by reducing alert firings and resulting clicks. The program also involved more users in the process of evaluating and improving CDS and helped build a culture of continuous evaluation and improvement of clinical content in the electronic health record.

Tracking health commodity inventory and notifying stock levels via mobile devices: a mixed methods systematic review

BACKGROUND

Health systems need timely and reliable access to essential medicines and health commodities, but problems with access are common in many settings. Mobile technologies offer potential low-cost solutions to the challenge of drug distribution and commodity availability in primary healthcare settings. However, the evidence on the use of mobile devices to address commodity shortages is sparse, and offers no clear way forward.

OBJECTIVES

Primary objective To assess the effects of strategies for notifying stock levels and digital tracking of healthcare-related commodities and inventory via mobile devices across the primary healthcare system Secondary objectives To describe what mobile device strategies are currently being used to improve reporting and digital tracking of health commodities To identify factors influencing the implementation of mobile device interventions targeted at reducing stockouts of health commodities SEARCH METHODS: We searched CENTRAL, MEDLINE Ovid, Embase Ovid, Global Index Medicus WHO, POPLINE K4Health, and two trials registries in August 2019. We also searched Epistemonikos for related systematic reviews and potentially eligible primary studies. We conducted a grey literature search using mHealthevidence.org, and issued a call for papers through popular digital health communities of practice. Finally, we conducted citation searches of included studies. We searched for studies published after 2000, in any language.

SELECTION CRITERIA

For the primary objective, we included individual and cluster-randomised trials, controlled before-after studies, and interrupted time series studies. For the secondary objectives, we included any study design, which could be quantitative, qualitative, or descriptive, that aimed to describe current strategies for commodity tracking or stock notification via mobile devices; or aimed to explore factors that influenced the implementation of these strategies, including studies of acceptability or feasibility. We included studies of all cadres of healthcare providers, including lay health workers, and others involved in the distribution of health commodities (administrative staff, managerial and supervisory staff, dispensary staff); and all other individuals involved in stock notification, who may be based in a facility or a community setting, and involved with the delivery of primary healthcare services. We included interventions aimed at improving the availability of health commodities using mobile devices in primary healthcare settings. For the primary objective, we included studies that compared health commodity tracking or stock notification via mobile devices with standard practice. For the secondary objectives, we included studies of health commodity tracking and stock notification via mobile device, if we could extract data relevant to our secondary objectives.

DATA COLLECTION AND ANALYSIS

For the primary objective, two authors independently screened all records, extracted data from the included studies, and assessed the risk of bias. For the analyses of the primary objectives, we reported means and proportions where appropriate. We used the GRADE approach to assess the certainty of the evidence, and prepared a 'Summary of findings' table. For the secondary objective, two authors independently screened all records, extracted data from the included studies, and applied a thematic synthesis approach to synthesise the data. We assessed methodological limitation using the Ways of Evaluating Important and Relevant Data (WEIRD) tool. We used the GRADE-CERQual approach to assess our confidence in the evidence, and prepared a 'Summary of qualitative findings' table.

MAIN RESULTS

Primary objective For the primary objective, we included one controlled before-after study conducted in Malawi. We are uncertain of the effect of cStock plus enhanced management, or cStock plus effective product transport on the availability of commodities, quality and timeliness of stock management, and satisfaction and acceptability, because we assessed the evidence as very low-certainty. The study did not report on resource use or unintended consequences. Secondary objective For the secondary objectives, we included 16 studies, using a range of study designs, which described a total of eleven interventions. All studies were conducted in African (Tanzania, Kenya, Malawi, Ghana, Ethiopia, Cameroon, Zambia, Liberia, Uganda, South Africa, and Rwanda) and Asian (Pakistan and India) countries. Most of the interventions aimed to make data about stock levels and potential stockouts visible to managers, who could then take corrective action to address them. We identified several factors that may influence the implementation of stock notification and tracking via mobile device. These include challenges tied to infrastructural issues, such as poor access to electricity or internet, and broader health systems issues, such as drug shortages at the national level which cannot be mitigated by interventions at the primary healthcare level (low confidence). Several factors were identified as important, including strong partnerships with local authorities, telecommunication companies, technical system providers, and non-governmental organizations (very low confidence); availability of stock-level data at all levels of the health system (low confidence); the role of supportive supervision and responsive management (moderate confidence); familiarity and training of health workers in the use of the digital devices (moderate confidence); availability of technical programming expertise for the initial development and ongoing maintenance of the digital systems (low confidence); incentives, such as phone credit for personal use, to support regular use of the system (low confidence); easy-to-use systems built with user participation (moderate confidence); use of basic or personal mobile phones to support easier adoption (low confidence); consideration for software features, such as two-way communication (low confidence); and data availability in an easy-to-use format, such as an interactive dashboard (moderate confidence).

AUTHORS' CONCLUSIONS

We need more, well-designed, controlled studies comparing stock notification and commodity management via mobile devices with paper-based commodity management systems. Further studies are needed to understand the factors that may influence the implementation of such interventions, and how implementation considerations differ by variations in the intervention.

The effect of the Continuous Care Model on treatment adherence in patients with myocardial infarction: a randomised controlled trial

Background

Adherence to treatment is one of the behaviours associated with successful outcomes following a myocardial infarction, which leads to successful treatment in the disease.

Aims

This study aimed to investigate the effect of the Continuous Care Model (CCM) on treatment adherence in patients with myocardial infarction.

Methods

This was a randomised controlled trial performed on 82 patients with myocardial infarction. Convenience sampling was used to select the participants, and then they were allocated into two groups by the stratified random method. In the intervention group, a CCM was implemented. In the intervention group, 4-6 educational sessions (1-2 h) were conducted during one month in the form of spoken questions and answers about the presented subjects. The control group received routine care. A questionnaire of demographic information and treatment adherence was completed by samples in the two groups, intervention and control, before and immediately after training and after follow-up.

Results

The results of this study showed that treatment adherence was significantly higher in the intervention group than in the control group immediately after training and after the follow-up phase (three months) (p < 0.001). Also, diet, drug and physical activity adherence were significantly higher in the intervention group than in the control group immediately after training and after follow-up (p < 0.001).

Conclusions

Implementation of CCM led to an increase in adherence to the treatment in patients with myocardial infarction. Therefore, it is suggested that this model could be used as a nursing intervention to increase treatment adherence in cardiac-rehabilitation programmes.

Interventions to Improve Medication Adherence: A Review

IMPORTANCE

Among adults with chronic illness, 30% to 50% of medications are not taken as prescribed. In the United States, it is estimated that medication nonadherence is associated with 125 000 deaths, 10% of hospitalizations, and $100 billion in health care services annually.

OBSERVATIONS

PubMed was searched from January 1, 2000, to September 6, 2018, for English-language randomized clinical trials of interventions to improve medication adherence. Trials of patients younger than 18 years, trials that used self-report as the primary adherence outcome, and trials with follow-up periods less than 6 months were excluded; 49 trials were included. The most common methods of identifying patients at risk for nonadherence were patient self-report, electronic drug monitors (pill bottles), or pharmacy claims data to measure gaps in supply. Patient self-report is the most practical method of identifying nonadherent patients in the context of clinical care but may overestimate adherence compared with objective methods such as electronic drug monitors and pharmacy claims data. Six categories of interventions, and characteristics of successful interventions within each category, were identified: patient education (eg, recurrent and personalized telephone counseling sessions with health educators); medication regimen management (using combination pills to reduce the number of pills patients take daily); clinical pharmacist consultation for chronic disease co-management (including education, increased frequency of disease monitoring via telephone or in-person follow-up visits, and refill reminders); cognitive behavioral therapies (such as motivational interviewing by trained counselors); medication-taking reminders (such as refill reminder calls or use of electronic drug monitors for real-time monitoring and reminding); and incentives to promote adherence (such as reducing co-payments and paying patients and clinicians for achieving disease management goals). The choice of intervention to promote adherence will depend on feasibility and availability within a practice or health system. Successful interventions that are also clinically practical include using combination pills to reduce daily pill burden, clinical pharmacist consultation for disease co-management, and medication-taking reminders such as telephone calls to prompt refills (maximum observed absolute improvements in adherence of 10%, 15%, and 33%, respectively).

CONCLUSIONS AND RELEVANCE

Adherence can be assessed and improved within the context of usual clinical care, but more intensive and costly interventions that have demonstrated success will require additional investments by health systems.

Providing physicians with feedback on medication adherence for people with chronic diseases taking long-term medication

BACKGROUND

Poor medication adherence decreases treatment efficacy and worsens clinical outcomes, but average rates of adherence to long-term pharmacological treatments for chronic illnesses are only about 50%. Interventions for improving medication adherence largely focus on patients rather than on physicians; however, the strategies shown to be effective are complex and difficult to implement in clinical practice. There is a need for new care models addressing the problem of medication adherence, integrating this problem into the patient care process. Physicians tend to overestimate how well patients take their medication as prescribed. This can lead to missed opportunities to change medications, solve adverse effects, or propose the use of reminders in order to improve patients' adherence. Thus, providing physicians with feedback on medication adherence has the potential to prompt changes that improve their patients' adherence to prescribed medications.

OBJECTIVES

To assess the effects of providing physicians with feedback about their patients' medication adherence for improving adherence. We also assessed the effects of the intervention on patient outcomes, health resource use, and processes of care.

SEARCH METHODS

We conducted a systematic search of the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, and Embase, all from database inception to December 2016 and without any language restriction. We also searched ISI Web of Science, two trials registers, and grey literature.

SELECTION CRITERIA

We included randomised trials, controlled before-after studies, and interrupted time series studies that compared the effects of providing feedback to physicians about their patients' adherence to prescribed long-term medications for chronic diseases versus usual care. We included published or unpublished studies in any language. Participants included any physician and any patient prescribed with long-term medication for chronic disease. We included interventions providing the prescribing physician with information about patient adherence to medication. Only studies in which feedback to the physician was the sole intervention or the essential component of a multifaceted intervention were eligible. In the comparison groups, the physicians should not have had access to information about their patients' adherence to medication. We considered the following outcomes: medication adherence, patient outcomes, health resource use, processes of care, and adverse events.

DATA COLLECTION AND ANALYSIS

Two independent review authors extracted and analysed all data using standard methodological procedures expected by Cochrane and the Effective Practice and Organisation of Care group. Due to heterogeneity in study methodology, comparison groups, intervention settings, and measurements of outcomes, we did not carry out meta-analysis. We describe the impact of interventions on outcomes in tabular form and make a qualitative assessment of the effects of studies.

MAIN RESULTS

We included nine studies (23,255 patient participants): eight randomised trials and one interrupted time series analysis. The studies took place in primary care and other outpatient settings in the USA and Canada. Seven interventions involved the systematic provision of feedback to physicians concerning all their patients' adherence to medication, and two interventions involved issuing an alert for non-adherent patients only. Seven studies used pharmacy refill data to assess medication adherence, and two used an electronic device or self-reporting. The definition of adherence differed across studies, making comparisons difficult. Eight studies were at high risk of bias, and one study was at unclear risk of bias. The most frequent source of bias was lack of protection against contamination.Providing physicians with feedback may lead to little or no difference in medication adherence (seven studies, 22,924 patients), patient outcomes (two studies, 1292 patients), or health resource use (two studies, 4181 patients). Providing physicians with feedback on medication adherence may improve processes of care (e.g. more medication changes, dialogue with patient, management of uncontrolled hypertension) compared to usual care (four studies, 2780 patients). None of the studies reported an adverse event due to the intervention. The certainty of evidence was low for all outcomes, mainly due to high risk of bias, high heterogeneity across studies, and indirectness of evidence.

AUTHORS' CONCLUSIONS

Across nine studies, we observed little or no evidence that provision of feedback to physicians regarding their patients adherence to prescribed medication improved medication adherence, patient outcomes, or health resource use. Feedback about medication adherence may improve processes of care, but due to the small number of studies assessing this outcome and high risk of bias, we cannot draw firm conclusions on the effect of feedback on this outcome. Future research should use a clear, standardised definition of medication adherence and cluster-randomisation to avoid the risk of contamination.

Putting evidence into practice: evidence-based interventions for oral agents for cancer

BACKGROUND

The limited evidence available suggests that adherence to oral agents for cancer is a significant clinical problem and may have a substantial impact on treatment success or failure. Adherence is a difficult issue among patients who are very sick with a life-threatening disease who often must adhere to complex treatment protocols independently at home.

OBJECTIVES

This article aims to identify effective interventions for the promotion, treatment, and management of adherence to oral agents for cancer and to synthesize the literature for use in clinical practice.

METHODS

As part of the Oncology Nursing Society (ONS) Putting Evidence Into Practice (PEP) initiative, a comprehensive examination of the current literature was conducted to identify effective interventions for patients prescribed oral agents for cancer. The ONS PEP weight-of-evidence classification schema levels of evidence were used to categorize interventions to assist nurses in identifying strategies that are effective at improving adherence.

FINDINGS

The majority of evidence found was conducted in conditions other than cancer; therefore, research is needed to identify whether these interventions are effective at promoting adherence in patients with cancer.

Interventions to improve adherence to lipid-lowering medication

BACKGROUND

Lipid-lowering drugs are widely underused, despite strong evidence indicating they improve cardiovascular end points. Poor patient adherence to a medication regimen can affect the success of lipid-lowering treatment.

OBJECTIVES

To assess the effects of interventions aimed at improving adherence to lipid-lowering drugs, focusing on measures of adherence and clinical outcomes.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, PsycINFO and CINAHL up to 3 February 2016, and clinical trials registers (ANZCTR and ClinicalTrials.gov) up to 27 July 2016. We applied no language restrictions.

SELECTION CRITERIA

We evaluated randomised controlled trials of adherence-enhancing interventions for lipid-lowering medication in adults in an ambulatory setting with a variety of measurable outcomes, such as adherence to treatment and changes to serum lipid levels. Two teams of review authors independently selected the studies.

DATA COLLECTION AND ANALYSIS

Three review authors extracted and assessed data, following criteria outlined by the Cochrane Handbook for Systematic Reviews of Interventions. We assessed the quality of the evidence using GRADEPro.

MAIN RESULTS

For this updated review, we added 24 new studies meeting the eligibility criteria to the 11 studies from prior updates. We have therefore included 35 studies, randomising 925,171 participants. Seven studies including 11,204 individuals compared adherence rates of those in an intensification of a patient care intervention (e.g. electronic reminders, pharmacist-led interventions, healthcare professional education of patients) versus usual care over the short term (six months or less), and were pooled in a meta-analysis. Participants in the intervention group had better adherence than those receiving usual care (odds ratio (OR) 1.93, 95% confidence interval (CI) 1.29 to 2.88; 7 studies; 11,204 participants; moderate-quality evidence). A separate analysis also showed improvements in long-term adherence rates (more than six months) using intensification of care (OR 2.87, 95% CI 1.91 to 4.29; 3 studies; 663 participants; high-quality evidence). Analyses of the effect on total cholesterol and LDL-cholesterol levels also showed a positive effect of intensified interventions over both short- and long-term follow-up. Over the short term, total cholesterol decreased by a mean of 17.15 mg/dL (95% CI 1.17 to 33.14; 4 studies; 430 participants; low-quality evidence) and LDL-cholesterol decreased by a mean of 19.51 mg/dL (95% CI 8.51 to 30.51; 3 studies; 333 participants; moderate-quality evidence). Over the long term (more than six months) total cholesterol decreased by a mean of 17.57 mg/dL (95% CI 14.95 to 20.19; 2 studies; 127 participants; high-quality evidence). Included studies did not report usable data for health outcome indications, adverse effects or costs/resource use, so we could not pool these outcomes. We assessed each included study for bias using methods described in the Cochrane Handbook for Systematic Reviews of Interventions. In general, the risk of bias assessment revealed a low risk of selection bias, attrition bias, and reporting bias. There was unclear risk of bias relating to blinding for most studies.

AUTHORS' CONCLUSIONS

The evidence in our review demonstrates that intensification of patient care interventions improves short- and long-term medication adherence, as well as total cholesterol and LDL-cholesterol levels. Healthcare systems which can implement team-based intensification of patient care interventions may be successful in improving patient adherence rates to lipid-lowering medicines.

Adjuvant Endocrine Therapy in Breast Cancer: A Novel e-Health Approach in Optimizing Treatment for Seniors (OPTIMUM): A Two-Group Controlled Comparison Pilot Study

Background

In women with hormone receptor positive breast cancer, adjuvant endocrine therapy (AET) is associated with a significant survival advantage. Nonadherence is a particular challenge in older women, even though they stand to benefit the most from AET. Therefore, a novel eHealth tool (OPTIMUM) that integrates real-time analysis of health administrative claims data was developed to provide point-of-care decision support for clinicians.

Objectives

The objectives of the study are to determine the effectiveness of a patient-specific, real-time eHealth alert delivered at point-of-care in reducing rates of AET discontinuation and to understand patient-level factors related to AET discontinuation as well as to assess integration of eHealth alerts regarding deviations from best practices in administration of AET by cancer care teams.

Methods

A prospective, 2-group controlled comparison pilot study will be conducted at 2 urban, McGill University–affiliated hospitals, the Royal Victoria Hospital and St. Mary’s Hospital. A minimum of 43 patients per study arm will be enrolled through site-level allocation. Follow-up is 1.5 years. Health care professionals at the intervention site will have access to the eHealth tool, which will report to them in real-time medical events with known associations to AET discontinuation, an AET adherence monitor, and a discontinuation alert. Cox proportional hazard ratios with 95% confidence intervals will estimate risks of AET discontinuation. Tests for significance will be 2-sided with a significance level of P<.05.

Results

This protocol has been approved and funded by the Canadian Institutes of Health Research. The study will evaluate site-level differences between AET discontinuation and AET adherence and assess care team actions at the intervention site. Participant enrollment into this project is expected to start September 2016 with primary data ready to present by June 2018.

Conclusion

This study will offer an opportunity to verify the feasibility of integrating an eHealth tool that aims to improve the long-term management of breast cancer in a high-risk population by allowing more timely intervention to prevent or rapidly address AET discontinuation.

Usability Evaluation of a Clinical Decision Support System for Geriatric ED Pain Treatment

BACKGROUND

Older adults are at risk for inadequate emergency department (ED) pain care. Unrelieved acute pain is associated with poor outcomes. Clinical decision support systems (CDSS) hold promise to improve patient care, but CDSS quality varies widely, particularly when usability evaluation is not employed.

OBJECTIVE

To conduct an iterative usability and redesign process of a novel geriatric abdominal pain care CDSS. We hypothesized this process would result in the creation of more usable and favorable pain care interventions.

METHODS

Thirteen emergency physicians familiar with the Electronic Health Record (EHR) in use at the study site were recruited. Over a 10-week period, 17 1-hour usability test sessions were conducted across 3 rounds of testing. Participants were given 3 patient scenarios and provided simulated clinical care using the EHR, while interacting with the CDSS interventions. Quantitative System Usability Scores (SUS), favorability scores and qualitative narrative feedback were collected for each session. Using a multi-step review process by an interdisciplinary team, positive and negative usability issues in effectiveness, efficiency, and satisfaction were considered, prioritized and incorporated in the iterative redesign process of the CDSS. Video analysis was used to determine the appropriateness of the CDS appearances during simulated clinical care.

RESULTS

Over the 3 rounds of usability evaluations and subsequent redesign processes, mean SUS progressively improved from 74.8 to 81.2 to 88.9; mean favorability scores improved from 3.23 to 4.29 (1 worst, 5 best). Video analysis revealed that, in the course of the iterative redesign processes, rates of physicians' acknowledgment of CDS interventions increased, however most rates of desired actions by physicians (such as more frequent pain score updates) decreased.

CONCLUSION

The iterative usability redesign process was instrumental in improving the usability of the CDSS; if implemented in practice, it could improve geriatric pain care. The usability evaluation process led to improved acknowledgement and favorability. Incorporating usability testing when designing CDSS interventions for studies may be effective to enhance clinician use.

Clinical Decision Support Systems and Prevention: A Community Guide Cardiovascular Disease Systematic Review

CONTEXT

Clinical decision support systems (CDSSs) can help clinicians assess cardiovascular disease (CVD) risk and manage CVD risk factors by providing tailored assessments and treatment recommendations based on individual patient data. The goal of this systematic review was to examine the effectiveness of CDSSs in improving screening for CVD risk factors, practices for CVD-related preventive care services such as clinical tests and prescribed treatments, and management of CVD risk factors.

EVIDENCE ACQUISITION

An existing systematic review (search period, January 1975-January 2011) of CDSSs for any condition was initially identified. Studies of CDSSs that focused on CVD prevention in that review were combined with studies identified through an updated search (January 2011-October 2012). Data analysis was conducted in 2013.

EVIDENCE SYNTHESIS

A total of 45 studies qualified for inclusion in the review. Improvements were seen for recommended screening and other preventive care services completed by clinicians, recommended clinical tests completed by clinicians, and recommended treatments prescribed by clinicians (median increases of 3.8, 4.0, and 2.0 percentage points, respectively). Results were inconsistent for changes in CVD risk factors such as systolic and diastolic blood pressure, total and low-density lipoprotein cholesterol, and hemoglobin A1C levels.

CONCLUSIONS

CDSSs are effective in improving clinician practices related to screening and other preventive care services, clinical tests, and treatments. However, more evidence is needed from implementation of CDSSs within the broad context of comprehensive service delivery aimed at reducing CVD risk and CVD-related morbidity and mortality.

Cross-vendor evaluation of key user-defined clinical decision support capabilities: a scenario-based assessment of certified electronic health records with guidelines for future development

OBJECTIVE

Clinical decision support (CDS) is essential for delivery of high-quality, cost-effective, and safe healthcare. The authors sought to evaluate the CDS capabilities across electronic health record (EHR) systems.

METHODS

We evaluated the CDS implementation capabilities of 8 Office of the National Coordinator for Health Information Technology Authorized Certification Body (ONC-ACB)-certified EHRs. Within each EHR, the authors attempted to implement 3 user-defined rules that utilized the various data and logic elements expected of typical EHRs and that represented clinically important evidenced-based care. The rules were: 1) if a patient has amiodarone on his or her active medication list and does not have a thyroid-stimulating hormone (TSH) result recorded in the last 12 months, suggest ordering a TSH; 2) if a patient has a hemoglobin A1c result >7% and does not have diabetes on his or her problem list, suggest adding diabetes to the problem list; and 3) if a patient has coronary artery disease on his or her problem list and does not have aspirin on the active medication list, suggest ordering aspirin.

RESULTS

Most evaluated EHRs lacked some CDS capabilities; 5 EHRs were able to implement all 3 rules, and the remaining 3 EHRs were unable to implement any of the rules. One of these did not allow users to customize CDS rules at all. The most frequently found shortcomings included the inability to use laboratory test results in rules, limit rules by time, use advanced Boolean logic, perform actions from the alert interface, and adequately test rules.

CONCLUSION

Significant improvements in the EHR certification and implementation procedures are necessary.

Effects of a computerized decision support system in improving pharmacological management in high-risk cardiovascular patients: A cluster-randomized open-label controlled trial

This study was aimed to investigate the effects of computerized decision support system in improving the prescription of drugs for cardiovascular prevention. A total of 197 Italian general practitioners were randomly allocated to receive either the alerting computerized decision support system integrated into standard software (intervention arm) or the standard software alone (control arm). Data on 21230 patients with diabetes, 3956 with acute myocardial infarction, and 2158 with stroke were analysed. The proportion of patients prescribed with cardiovascular drugs and days of drug–drug interaction exposure were evaluated. Computerized decision support system significantly increased the proportion of patients with diabetes prescribed with antiplatelet drugs (intervention: +2.7% vs. control: +0.15%; p < 0.001) or lipidlowering drugs (+4.2% vs. +2.8%; p = 0.001). A statistically significant decrease in days of potential interactions has been observed only among patients with stroke (−1.2 vs. −0.5 days/person-year; p = 0.001). In conclusion, computerized decision support system significantly increased the use of recommended cardiovascular drugs in diabetic patients, but it did not influence the exposure to potential interactions

Computerized advice on drug dosage to improve prescribing practice

BACKGROUND

Maintaining therapeutic concentrations of drugs with a narrow therapeutic window is a complex task. Several computer systems have been designed to help doctors determine optimum drug dosage. Significant improvements in health care could be achieved if computer advice improved health outcomes and could be implemented in routine practice in a cost-effective fashion. This is an updated version of an earlier Cochrane systematic review, first published in 2001 and updated in 2008.

OBJECTIVES

To assess whether computerized advice on drug dosage has beneficial effects on patient outcomes compared with routine care (empiric dosing without computer assistance).

SEARCH METHODS

The following databases were searched from 1996 to January 2012: EPOC Group Specialized Register, Reference Manager; Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Ovid; EMBASE, Ovid; and CINAHL, EbscoHost. A "top up" search was conducted for the period January 2012 to January 2013; these results were screened by the authors and potentially relevant studies are listed in Studies Awaiting Classification. The review authors also searched reference lists of relevant studies and related reviews.

SELECTION CRITERIA

We included randomized controlled trials, non-randomized controlled trials, controlled before-and-after studies and interrupted time series analyses of computerized advice on drug dosage. The participants were healthcare professionals responsible for patient care. The outcomes were any objectively measured change in the health of patients resulting from computerized advice (such as therapeutic drug control, clinical improvement, adverse reactions).

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data and assessed study quality. We grouped the results from the included studies by drug used and the effect aimed at for aminoglycoside antibiotics, amitriptyline, anaesthetics, insulin, anticoagulants, ovarian stimulation, anti-rejection drugs and theophylline. We combined the effect sizes to give an overall effect for each subgroup of studies, using a random-effects model. We further grouped studies by type of outcome when appropriate (i.e. no evidence of heterogeneity).

MAIN RESULTS

Forty-six comparisons (from 42 trials) were included (as compared with 26 comparisons in the last update) including a wide range of drugs in inpatient and outpatient settings. All were randomized controlled trials except two studies. Interventions usually targeted doctors, although some studies attempted to influence prescriptions by pharmacists and nurses. Drugs evaluated were anticoagulants, insulin, aminoglycoside antibiotics, theophylline, anti-rejection drugs, anaesthetic agents, antidepressants and gonadotropins. Although all studies used reliable outcome measures, their quality was generally low.This update found similar results to the previous update and managed to identify specific therapeutic areas where the computerized advice on drug dosage was beneficial compared with routine care:1. it increased target peak serum concentrations (standardized mean difference (SMD) 0.79, 95% CI 0.46 to 1.13) and the proportion of people with plasma drug concentrations within the therapeutic range after two days (pooled risk ratio (RR) 4.44, 95% CI 1.94 to 10.13) for aminoglycoside antibiotics;2. it led to a physiological parameter more often within the desired range for oral anticoagulants (SMD for percentage of time spent in target international normalized ratio +0.19, 95% CI 0.06 to 0.33) and insulin (SMD for percentage of time in target glucose range: +1.27, 95% CI 0.56 to 1.98);3. it decreased the time to achieve stabilization for oral anticoagulants (SMD -0.56, 95% CI -1.07 to -0.04);4. it decreased the thromboembolism events (rate ratio 0.68, 95% CI 0.49 to 0.94) and tended to decrease bleeding events for anticoagulants although the difference was not significant (rate ratio 0.81, 95% CI 0.60 to 1.08). It tended to decrease unwanted effects for aminoglycoside antibiotics (nephrotoxicity: RR 0.67, 95% CI 0.42 to 1.06) and anti-rejection drugs (cytomegalovirus infections: RR 0.90, 95% CI 0.58 to 1.40);5. it tended to reduce the length of time spent in the hospital although the difference was not significant (SMD -0.15, 95% CI -0.33 to 0.02) and to achieve comparable or better cost-effectiveness ratios than usual care;6. there was no evidence of differences in mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants.For all outcomes, statistical heterogeneity quantified by I(2) statistics was moderate to high.

AUTHORS' CONCLUSIONS

This review update suggests that computerized advice for drug dosage has some benefits: it increases the serum concentrations for aminoglycoside antibiotics and improves the proportion of people for which the plasma drug is within the therapeutic range for aminoglycoside antibiotics.It leads to a physiological parameter more often within the desired range for oral anticoagulants and insulin. It decreases the time to achieve stabilization for oral anticoagulants. It tends to decrease unwanted effects for aminoglycoside antibiotics and anti-rejection drugs, and it significantly decreases thromboembolism events for anticoagulants. It tends to reduce the length of hospital stay compared with routine care while comparable or better cost-effectiveness ratios were achieved.However, there was no evidence that decision support had an effect on mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants. In addition, there was no evidence to suggest that some decision support technical features (such as its integration into a computer physician order entry system) or aspects of organization of care (such as the setting) could optimize the effect of computerized advice.Taking into account the high risk of bias of, and high heterogeneity between, studies, these results must be interpreted with caution.

Targeting cardiovascular medication adherence interventions

OBJECTIVES

To determine whether adherence interventions should be administered to all medication takers or targeted to nonadherers.

DATA SOURCES AND STUDY SELECTION

Systematic search (Medline and Embase, 1966-2009) of randomized controlled trials of interventions to improve adherence to medications for preventing or treating cardiovascular disease or diabetes.

DATA EXTRACTION

Articles were classified as (1) broad interventions (targeted all medication takers), (2) focused interventions (targeted nonadherers), or (3) dynamic interventions (administered to all medication takers; real-time adherence information targets nonadherers as intervention proceeds). Cohen's d effect sizes were calculated.

DATA SYNTHESIS

We identified 7,190 articles; 59 met inclusion criteria. Broad interventions were less likely (18%) to show medium or large effects compared with focused (25%) or dynamic (32%) interventions. Of the 33 dynamic interventions, 6 used externally generated adherence data to target nonadherers. Those with externally generated data were less likely to have a medium or large effect (20% vs. 34.8% self-generated data).

CONCLUSION

Adherence interventions targeting nonadherers are heterogeneous but may have advantages over broad interventions. Dynamic interventions show promise and require further study.

The effectiveness of a new generation of computerized drug alerts in reducing the risk of injury from drug side effects: a cluster randomized trial

CONTEXT

Computerized drug alerts for psychotropic drugs are expected to reduce fall-related injuries in older adults. However, physicians over-ride most alerts because they believe the benefit of the drugs exceeds the risk.

OBJECTIVE

To determine whether computerized prescribing decision support with patient-specific risk estimates would increase physician response to psychotropic drug alerts and reduce injury risk in older people.

DESIGN

Cluster randomized controlled trial of 81 family physicians and 5628 of their patients aged 65 and older who were prescribed psychotropic medication.

INTERVENTION

Intervention physicians received information about patient-specific risk of injury computed at the time of each visit using statistical models of non-modifiable risk factors and psychotropic drug doses. Risk thermometers presented changes in absolute and relative risk with each change in drug treatment. Control physicians received commercial drug alerts.

MAIN OUTCOME MEASURES

Injury risk at the end of follow-up based on psychotropic drug doses and non-modifiable risk factors. Electronic health records and provincial insurance administrative data were used to measure outcomes.

RESULTS

Mean patient age was 75.2 years. Baseline risk of injury was 3.94 per 100 patients per year. Intermediate-acting benzodiazepines (56.2%) were the most common psychotropic drug. Intervention physicians reviewed therapy in 83.3% of visits and modified therapy in 24.6%. The intervention reduced the risk of injury by 1.7 injuries per 1000 patients (95% CI 0.2/1000 to 3.2/1000; p=0.02). The effect of the intervention was greater for patients with higher baseline risks of injury (p<0.03).

CONCLUSION

Patient-specific risk estimates provide an effective method of reducing the risk of injury for high-risk older people.

TRIAL REGISTRATION NUMBER

clinicaltrials.gov Identifier: NCT00818285.

Comparing Medication Adherence Methods in Lipid-Modifying Therapy

Background:

Investigators have employed a number of different methods to calculate adherence estimates for patients taking lipid-modifying therapy (LMT), including measures with different numerator and denominator options. Although at least one method is known to correlate well with cardiovascular outcomes, most have not been evaluated in outcomes studies.

Objectives:

To evaluate different methods for measuring adherence, using LMT as a case example, and to determine whether estimates for adherence differ statistically and/or whether different methods can lead to different conclusions about patient adherence.

Methods:

Adherence ratios were calculated using 8 different methods for 12,448 patients who were in a managed-care system and were considered new starts with statin therapy. The calculated measures were compared and tested for differences. Patients were categorized as adherent by each method, using a threshold of 0.8, and the proportions of patients categorized as adherent were compared for differences between adherence calculation methods.

Results:

Adherence ratios calculated with like observation intervals did not vary substantially, regardless of which method for measuring medication availability was used. Those calculated with different observation intervals had substantial variability. Mean adherence ratios ranged between 0.777 and 0.798 for difference in days' observation intervals; they ranged between 0.618 and 0.630 for the predefined interval. Differences between ratios calculated using these different denominators were statistically significant (p < 0.008). Correlations between ratios were statistically significant for all comparisons (p < 0.001). Correlation coefficients ( r) were 0.64 for comparisons between ratios with different denominators versus 1.0 for comparisons with like denominators. Categorization as adherent or nonadherent differed between the methods for about 20% of patients.

Conclusions:

Significant differences were found to be based on observation period but not on medication availability. Studies of adherence should be interpreted with caution depending on which method is used, and particular interest should be paid to whether the choice of methods is consistent with study objectives and to the observation interval, as different methods may lead to different conclusions about patient adherence. Further research in LMT and other therapeutic areas is needed to determine which methods correlate best with positive patient outcomes, such as reductions in low-density lipoprotein cholesterol and cardiovascular events.

The roles of past behavior and health beliefs in predicting medication adherence to a statin regimen

PURPOSE

Current medication-adherence predictive tools are based on patient medication-taking beliefs, but studying past behavior may now be a more explanatory and accessible method. This study will evaluate if past medication-refill behavior for a statin regimen is more predictive of medication adherence than patient medication-taking health beliefs.

PATIENTS AND METHODS

This prospective longitudinal study was implemented in a national managed care plan in the United States. A group of 1433 statin patients were identified and followed for 6 months. Medication-taking health beliefs, collected from self-reported mail questionnaires, and past medication-refill behavior, using proportion of days covered (PDC), were collected prior to 6-month follow-up. Outcomes were measured using categorical PDC variable (of adherence, PDC ≥ 85%, versus nonadherence, PDC < 85%), with model fit estimated using receiver operator characteristic analysis.

RESULTS

The area under the receiver operator characteristic curve for past behavior (A(z) = 0.78) was significantly greater (P < 0.05) than for patient health beliefs (A(z) = 0.69), indicating that past prescription-refill behavior is a better predictor of medication adherence than prospective health beliefs. Among health beliefs, the factor most related to medication adherence was behavioral intent (odds ratio, 5.12; 95% confidence interval, 1.84 to 15.06). The factor most strongly related to behavioral intent was impact of regimen on daily routine (odds ratio, 3.3; 95% confidence interval, 1.41 to 7.74).

CONCLUSION

Electronic medical records and community health-information networks may make past prescription-refill rates more accessible and assist physicians with managing medication-regimen adherence. Health beliefs, however, may still play an important role in influencing medication-taking behaviors.

The effectiveness of integrated health information technologies across the phases of medication management: a systematic review of randomized controlled trials

OBJECTIVE

The US Agency for Healthcare Research and Quality funded an evidence report to address seven questions on multiple aspects of the effectiveness of medication management information technology (MMIT) and its components (prescribing, order communication, dispensing, administering, and monitoring).

MATERIALS AND METHODS

Medline and 11 other databases without language or date limitations to mid-2010. Randomized controlled trials (RCTs) assessing integrated MMIT were selected by two independent reviewers. Reviewers assessed study quality and extracted data. Senior staff checked accuracy.

RESULTS

Most of the 87 RCTs focused on clinical decision support and computerized provider order entry systems, were performed in hospitals and clinics, included primarily physicians and sometimes nurses but not other health professionals, and studied process changes related to prescribing and monitoring medication. Processes of care improved for prescribing and monitoring mostly in hospital settings, but the few studies measuring clinical outcomes showed small or no improvements. Studies were performed most frequently in the USA (n=63), Europe (n=16), and Canada (n=6).

DISCUSSION

Many studies had limited description of systems, installations, institutions, and targets of the intervention. Problems with methods and analyses were also found. Few studies addressed order communication, dispensing, or administering, non-physician prescribers or pharmacists and their MMIT tools, or patients and caregivers. Other study methods are also needed to completely understand the effects of MMIT.

CONCLUSIONS

Almost half of MMIT interventions improved the process of care, but few studies measured clinical outcomes. This large body of literature, although instructive, is not uniformly distributed across settings, people, medication phases, or outcomes.

Surveillance of medication use: early identification of poor adherence

BACKGROUND

We sought to measure population-level adherence to antihyperlipidemics, antihypertensives, and oral hypoglycemics, and to develop a model for early identification of subjects at high risk of long-term poor adherence.

METHODS

Prescription-filling data for 2 million subjects derived from a payor's insurance claims were used to evaluate adherence to three chronic drugs over 1 year. We relied on patterns of prescription fills, including the length of gaps in medication possession, to measure adherence among subjects and to build models for predicting poor long-term adherence.

RESULTS

All prescription fills for a specific drug were sequenced chronologically into drug eras. 61.3% to 66.5% of the prescription patterns contained medication gaps >30 days during the first year of drug use. These interrupted drug eras include long-term discontinuations, where the subject never again filled a prescription for any drug in that category in the dataset, which represent 23.7% to 29.1% of all drug eras. Among the prescription-filling patterns without large medication gaps, 0.8% to 1.3% exhibited long-term poor adherence. Our models identified these subjects as early as 60 days after the first prescription fill, with an area under the curve (AUC) of 0.81. Model performance improved as the predictions were made at later time-points, with AUC values increasing to 0.93 at the 120-day time-point.

CONCLUSIONS

Dispensed medication histories (widely available in real time) are useful for alerting providers about poorly adherent patients and those who will be non-adherent several months later. Efforts to use these data in point of care and decision support facilitating patient are warranted.

Medication adherence: emerging use of technology

PURPOSE OF REVIEW

Adherence to proven, effective medications remains low, resulting in high rates of clinical complications, hospital readmissions, and death. The use of technology to identify patients at risk and to target interventions for poor adherence has increased. This review focuses on research that tests these emerging technologies and evaluates the effect of technology-based adherence interventions on cardiovascular outcomes.

RECENT FINDINGS

Recent studies have evaluated technology-based interventions to improve medication adherence by using pharmaceutical databases, tailoring educational information to individual patient needs, delivering technology-driven reminders to patients and providers, and integrating in-person interventions with electronic alerts. Cellular phone reminders and in-home electronic technology used to communicate reminder messages have shown mixed results. Only one study has shown improvement in both adherence and clinical outcome. Current trials suggest that increasing automated reminders will complement but not replace the benefits seen with in-person communication for medication taking.

SUMMARY

Integration of in-person contacts with technology-driven medication adherence reminders, electronic medication reconciliation, and pharmaceutical databases may improve medication adherence and have a positive effect on cardiovascular clinical outcomes. Opportunities for providers to monitor the quality of care based on new adherence research are evolving and may be useful as standards for quality improvement emerge.

A framework for evaluating the appropriateness of clinical decision support alerts and responses

OBJECTIVE

Alerting systems, a type of clinical decision support, are increasingly prevalent in healthcare, yet few studies have concurrently measured the appropriateness of alerts with provider responses to alerts. Recent reports of suboptimal alert system design and implementation highlight the need for better evaluation to inform future designs. The authors present a comprehensive framework for evaluating the clinical appropriateness of synchronous, interruptive medication safety alerts.

METHODS

Through literature review and iterative testing, metrics were developed that describe successes, justifiable overrides, provider non-adherence, and unintended adverse consequences of clinical decision support alerts. The framework was validated by applying it to a medication alerting system for patients with acute kidney injury (AKI).

RESULTS

Through expert review, the framework assesses each alert episode for appropriateness of the alert display and the necessity and urgency of a clinical response. Primary outcomes of the framework include the false positive alert rate, alert override rate, provider non-adherence rate, and rate of provider response appropriateness. Application of the framework to evaluate an existing AKI medication alerting system provided a more complete understanding of the process outcomes measured in the AKI medication alerting system. The authors confirmed that previous alerts and provider responses were most often appropriate.

CONCLUSION

The new evaluation model offers a potentially effective method for assessing the clinical appropriateness of synchronous interruptive medication alerts prior to evaluating patient outcomes in a comparative trial. More work can determine the generalizability of the framework for use in other settings and other alert types.

The effects of on-screen, point of care computer reminders on processes and outcomes of care

BACKGROUND

The opportunity to improve care by delivering decision support to clinicians at the point of care represents one of the main incentives for implementing sophisticated clinical information systems. Previous reviews of computer reminder and decision support systems have reported mixed effects, possibly because they did not distinguish point of care computer reminders from e-mail alerts, computer-generated paper reminders, and other modes of delivering 'computer reminders'.

OBJECTIVES

To evaluate the effects on processes and outcomes of care attributable to on-screen computer reminders delivered to clinicians at the point of care.

SEARCH STRATEGY

We searched the Cochrane EPOC Group Trials register, MEDLINE, EMBASE and CINAHL and CENTRAL to July 2008, and scanned bibliographies from key articles.

SELECTION CRITERIA

Studies of a reminder delivered via a computer system routinely used by clinicians, with a randomised or quasi-randomised design and reporting at least one outcome involving a clinical endpoint or adherence to a recommended process of care.

DATA COLLECTION AND ANALYSIS

Two authors independently screened studies for eligibility and abstracted data. For each study, we calculated the median improvement in adherence to target processes of care and also identified the outcome with the largest such improvement. We then calculated the median absolute improvement in process adherence across all studies using both the median outcome from each study and the best outcome.

MAIN RESULTS

Twenty-eight studies (reporting a total of thirty-two comparisons) were included. Computer reminders achieved a median improvement in process adherence of 4.2% (interquartile range (IQR): 0.8% to 18.8%) across all reported process outcomes, 3.3% (IQR: 0.5% to 10.6%) for medication ordering, 3.8% (IQR: 0.5% to 6.6%) for vaccinations, and 3.8% (IQR: 0.4% to 16.3%) for test ordering. In a sensitivity analysis using the best outcome from each study, the median improvement was 5.6% (IQR: 2.0% to 19.2%) across all process measures and 6.2% (IQR: 3.0% to 28.0%) across measures of medication ordering. In the eight comparisons that reported dichotomous clinical endpoints, intervention patients experienced a median absolute improvement of 2.5% (IQR: 1.3% to 4.2%). Blood pressure was the most commonly reported clinical endpoint, with intervention patients experiencing a median reduction in their systolic blood pressure of 1.0 mmHg (IQR: 2.3 mmHg reduction to 2.0 mmHg increase).

AUTHORS' CONCLUSIONS

Point of care computer reminders generally achieve small to modest improvements in provider behaviour. A minority of interventions showed larger effects, but no specific reminder or contextual features were significantly associated with effect magnitude. Further research must identify design features and contextual factors consistently associated with larger improvements in provider behaviour if computer reminders are to succeed on more than a trial and error basis.