Cost of illness of stage IV non-small cell lung cancer (NSCLC) positive for programmed cell death ligand 1 (PD-L1) in the US.

BACKGROUND: Non-small cell lung cancer (NSCLC) positive for programmed cell death ligand 1 (PD-L1) may be eligible for targeted immunotherapies. Literature does not currently estimate direct costs associated with this population. We aimed to identify the total direct costs associated with PD-L1 positive stage IV NSCLC treated with immunotherapy. METHODS: Using progression-free survival, overall survival, treatment-related serious adverse events leading to hospitalization, and end-of-life resource use, we estimated costs for one year of treatment in this incidence-based study. Data were obtained from online databases, guideline recommendations, clinical trials, and proprietary market share data. We summed the costs of PD-L1 immunohistochemistry (IHC) tests, drugs, hospitalizations, and deaths associated with treatment, estimating the overall cost-of-illness for stage IV NSCLC in the United States in 2021. RESULTS: An estimated 22,711 patients in the US had stage IV NSCLC treated with PD-L1 immunotherapy in 2021. Total 2021 costs were estimated at $3.01 billion. Drugs (including immunotherapy, second-line chemotherapy, and other oncology drugs) accounted for nearly 97% ($2.91 billion) of the total. CONCLUSIONS: PD-L1 positive stage IV NSCLC treatment is a costly condition with annual direct medical costs of $3.01 billion. The primary cost driver was immunotherapy, making up 74.6% of the total cost.

Cost-Effectiveness of PD-L1 Testing in Non-Small Cell Lung Cancer (NSCLC) Using In Vitro Diagnostic (IVD) Versus Laboratory-Developed Test (LDT).

INTRODUCTION: Accurate PD-L1 testing for non-small cell lung cancer (NSCLC) maximizes the benefits of immune checkpoint inhibitor (ICI) drugs like pembrolizumab. False negative test results deny ICI treatments to eligible patients, worsening clinical and economic outcomes, while false positives increase costs by using ICI treatments without their benefits. This study evaluates the cost-effectiveness of PD-L1 testing with an in vitro diagnostic (IVD) compared to a laboratory-developed test (LDT) for allocating patients with NSCLC to treatment with either pembrolizumab or chemotherapy using the German healthcare system as a model. METHODS: We developed a decision analytical model to evaluate the cost-effectiveness of PD-L1 testing with a regulatory body approved IVD compared to an LDT from the national German healthcare payer (statutory health insurance system) perspective. Accuracy of PD-L1 testing was based on data from two independent proficiency testing programs. The 1-year model was based on outcomes data from the KEYNOTE-024 clinical trial and treatment patterns reflecting current German practices. RESULTS: IVDs produced accurate PD-L1 testing results in 93% (752/811) of tested cases compared to 73% (492/672) with LDTs. Most misclassifications concerned false negatives, occurring in 21% of LDTs vs 7% of IVDs. Total costs of the IVD group (48,878 €) were 196 € higher than the LDT group (48,682 €). These costs incorporate testing, first- and second-line therapy, managing treatment-related grade 3+ adverse events (AEs), and end-of-life costs for those who died within the year. Total effectiveness (percentage of patients successfully diagnosed and prescribed the correct therapy per German treatment guidelines) was 19 percentage points higher for the IVD group (88%) compared to the LDT group (69%). These differences in costs and effects lead to an incremental cost-effectiveness ratio (ICER) of 1057 €. CONCLUSION: Compared to LDT technology, on-label IVD use for PD-L1 testing is only slightly more costly and substantially more effective for aligning patients with PD-L1-positive NSCLC with ICI therapy according to German practice guidelines. Given these findings, changes to testing and reimbursement policies may be considered to maximize patient outcomes in NSCLC.

Cost-effectiveness of once-daily vs twice-daily tacrolimus among Hispanic and Black kidney transplant recipients.

BACKGROUND: Tacrolimus is a first-line immunosuppressive therapy to prevent rejection and graft failure in kidney transplant recipients. Once-daily extended-release tacrolimus tablets (LCPT) have been shown to be efficacious, particularly for Hispanic and Black patient subpopulations who are rapid metabolizers, but is more costly than twice-daily immediate-release tacrolimus (IR-Tac). OBJECTIVE: To evaluate the cost-effectiveness of LCPT during the first year of treatment vs IR-Tac in kidney transplant recipients who are Hispanic or Black. METHODS: A decision analytic model from a US payer perspective was developed using (1) subgroup outcomes data pooled from two phase 3 clinical trials that compared LCPT and IR-Tac, and (2) direct costs from real-world data sources (ie, costs of LCPT and IR-Tac treatments, biopsy-proven acute rejection, treatment-related serious adverse events [SAEs], graft failure, and consequent dialysis). The primary outcome was cost per successfully treated patient, defined as having a functioning graft after 1 year and without treatment-related SAEs. Probabilistic sensitivity analyses established distributions for cost and outcomes estimates, and a series of one-way sensitivity analyses identified parameters that had the most effect on results. RESULTS: Total overall cost for the Hispanic group was $14,765 for LCPT and $12,416 for IR-Tac, and total cost in the Black group was $16,626 for LCPT and $9,871 for IR-Tac. Total overall effectiveness of LCPT and IR-Tac was 88.32% and 84.75% in the Hispanic group and 93.24% and 85.78% in the Black group, respectively. The incremental cost-effectiveness ratio (ICER) for using LCPT over IR-Tac during the first year of treatment in the Hispanic group was $65,643 per additional successfully treated patient. The ICER for the Black group was $90,458. The single parameter having the most impact on results in both groups was the probability of a treatment-related SAE in IR-Tac, which accounted for 49% of variation in results in the Hispanic group and 46% in the Black group. CONCLUSIONS: Overall results for both groups show that LCPT is incrementally more costly and more effective compared with IR-Tac, indicating a trade-off scenario. LCPT is a cost-effective strategy if a decision makers' willingness to pay for 1 additional successfully treated patient exceeds the ICER and must be weighed against the costs of graft loss, continuing dialysis, and potential retransplant. This study provides a foundation for further research to update and expand inputs as more data become available to improve real-world relevance and decision making. DISCLOSURES: This study was funded by Veloxis Pharmaceuticals, Inc., which provided clinical trial file data and nonbinding feedback on the model structure, data interpretation, clinical expertise, manuscript review, and areas of publication interest (ie, managed care). Hurwitz, Grizzle, Villa Zapata, and Malone received grant funding from Veloxis Pharmaceuticals, Inc., through University of Arizona to conduct research and analysis for this study. Tyler is employed by Veloxis Pharmaceuticals, Inc. Some of the data reported and used in this research were available from the US Renal Data System, the US Bureau of Labor Statistics, and the Agency for Healthcare Research and Quality's Healthcare Cost and Utility Project. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US government.

Clinical and Economic Burden of Valley Fever in Arizona: An Incidence-Based Cost-of-Illness Analysis.

BACKGROUND: Coccidioidomycosis, ie, Valley fever, is an important fungal infection in the Southwest, with half to two thirds of all cases occurring in Arizona. This endemic respiratory disease can range from primary uncomplicated pneumonia to disseminated infection such as meningitis with chronic pulmonary complications. Valley fever diagnoses have risen over recent years and cause substantial morbidity and economic burden in Arizona. METHODS: We estimated the lifetime cost-of-illness associated with all cases of Valley fever diagnosed in 2019 in Arizona. Natural history of the disease was determined from literature and expert opinion and assigned costs from national data sources to determine lifetime direct and indirect costs (work loss). RESULTS: Total lifetime costs of $736 million were estimated for the 10 359 cases of Valley fever diagnosed in Arizona in 2019. Direct costs of $671 million accounted for over 90% of expenditures, with $65 million in indirect costs. Disseminated infection produces the highest economic burden at $1.26 million direct and $137 400 indirect costs per person. The lowest Valley fever lifetime costs were for cases of primary uncomplicated pneumonia with $23 200 in direct costs and $1300 in lost wages. The average lifetime direct costs across all Valley fever manifestations are $64 800 per person diagnosed in Arizona in 2019 and $6300 for indirect costs. CONCLUSIONS: Valley fever is responsible for substantial economic burden in Arizona. Our estimates underscore the value of supporting research into developing more rapid diagnostic tests, better therapies, and ultimately a preventative vaccine to address this important public health problem in Arizona.

Testing the face validity and inter-rater agreement of a simple approach to drug-drug interaction evidence assessment.

Low concordance between drug-drug interaction (DDI) knowledge bases is a well-documented concern. One potential cause of inconsistency is variability between drug experts in approach to assessing evidence about potential DDIs. In this study, we examined the face validity and inter-rater reliability of a novel DDI evidence evaluation instrument designed to be simple and easy to use. METHODS: A convenience sample of participants with professional experience evaluating DDI evidence was recruited. Participants independently evaluated pre-selected evidence items for 5 drug pairs using the new instrument. For each drug pair, participants labeled each evidence item as sufficient or insufficient to establish the existence of a DDI based on the evidence categories provided by the instrument. Participants also decided if the overall body of evidence supported a DDI involving the drug pair. Agreement was computed both at the evidence item and drug pair levels. A cut-off of ≥ 70% was chosen as the agreement threshold for percent agreement, while a coefficient > 0.6 was used as the cut-off for chance-corrected agreement. Open ended comments were collected and coded to identify themes related to the participants' experience using the novel approach. RESULTS: The face validity of the new instrument was established by two rounds of evaluation involving a total of 6 experts. Fifteen experts agreed to participate in the reliability assessment, and 14 completed the study. Participant agreement on the sufficiency of 22 of the 34 evidence items (65%) did not exceed the a priori agreement threshold. Similarly, agreement on the sufficiency of evidence for 3 of the 5 drug pairs (60%) was poor. Chance-corrected agreement at the drug pair level further confirmed the poor interrater reliability of the instrument (Gwet's AC1 = 0.24, Conger's Kappa = 0.24). Participant comments suggested several possible reasons for the disagreements including unaddressed subjectivity in assessing an evidence item's type and study design, an infeasible separation of evidence evaluation from the consideration of clinical relevance, and potential issues related to the evaluation of DDI case reports. CONCLUSIONS: Even though the key findings were negative, the study's results shed light on how experts approach DDI evidence assessment, including the importance situating evidence assessment within the context of consideration of clinical relevance. Analysis of participant comments within the context of the negative findings identified several promising future research directions including: novel computer-based support for evidence assessment; formal evaluation of a more comprehensive evidence assessment approach that requires consideration of specific, explicitly stated, clinical consequences; and more formal investigation of DDI case report assessment instruments.

Real-World Evidence from the Integrative Medicine Primary Care Trial (IMPACT): Assessing Patient-Reported Outcomes at Baseline and 12-Month Follow-Up.

PURPOSE: The University of Arizona Integrative Health Center (UAIHC) was an innovative membership-supported integrative medicine (IM) adult primary care clinic in Phoenix, Arizona. UAIHC delivered healthcare using an integrative medicine model that combined conventional and complementary medical treatments, including nutrition, mind-body medicine, acupuncture, manual medicine, health coaching, educational classes, and groups. Results from pre-post evaluation of patient-reported outcomes on several standardized measures are presented here. METHODS: UAIHC patients completed surveys at baseline and after 12 months of continuous integrative primary care. Patients reported on perceived changes in health outcomes as measured by Short-Form Health Survey (SF-12 general, mental, and physical health), Perceived Stress Scale (PSS4), Work Productivity and Activity Impairment Questionnaire (WPAI), World Health Organization Well-Being Index (WHO-5), Pain Visual Analog Scale (VAS), Fatigue Severity Scale (VAS; FSS), Generalized Anxiety Disorder Scale (GAD2), Patient Health Questionnaire for depression (PHQ2), Pittsburgh Sleep Quality Index (PSQI) global rating of sleep quality, and the Behavioral Risk Factor Surveillance System (BRFSS; nutrition, exercise, and physical activity). Overall differences between time points were assessed for statistical significance. Patient demographics are also described. RESULTS: 177 patients completed baseline and follow-up outcome measures. Patients were predominantly white, female, college-educated, and employed. Baseline to one-year follow-up results indicate statistically significant improvements (p <.05) on all but perceived stress (PSS-4) and work absenteeism (WPAI). Clinical impact and/or practical effects are reported as percent change or standardized effect sizes whenever possible. Other demographic and descriptive information is summarized. CONCLUSIONS: Following one year of IM primary care at UAIHC, patient-reported outcomes indicated positive impacts in several areas of patients' lives: mental, physical, and overall health; work productivity; sleep quality; pain; fatigue; overall well-being; and physical activity.

Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey.

BACKGROUND: Preventing drug interactions is an important goal to maximize patient benefit from medications. Summarizing potential drug-drug interactions (PDDIs) for clinical decision support is challenging, and there is no single repository for PDDI evidence. Additionally, inconsistencies across compendia and other sources have been well documented. Standard search strategies for complete and current evidence about PDDIs have not heretofore been developed or validated. OBJECTIVE: This study aimed to identify common methods for conducting PDDI literature searches used by experts who routinely evaluate such evidence. METHODS: We invited a convenience sample of 70 drug information experts, including compendia editors, knowledge-base vendors, and clinicians, via emails to complete a survey on identifying PDDI evidence. We created a Web-based survey that included questions regarding the (1) development and conduct of searches; (2) resources used, for example, databases, compendia, search engines, etc; (3) types of keywords used to search for the specific PDDI information; (4) study types included and excluded in searches; and (5) search terms used. Search strategy questions focused on 6 topics of the PDDI information-(1) that a PDDI exists; (2) seriousness; (3) clinical consequences; (4) management options; (5) mechanism; and (6) health outcomes. RESULTS: Twenty participants (response rate, 20/70, 29%) completed the survey. The majority (17/20, 85%) were drug information specialists, drug interaction researchers, compendia editors, or clinical pharmacists, with 60% (12/20) having >10 years' experience. Over half (11/20, 55%) worked for clinical solutions vendors or knowledge-base vendors. Most participants developed (18/20, 90%) and conducted (19/20, 95%) search strategies without librarian assistance. PubMed (20/20, 100%) and Google Scholar (11/20, 55%) were most commonly searched for papers, followed by Google Web Search (7/20, 35%) and EMBASE (3/20, 15%). No respondents reported using Scopus. A variety of subscription and open-access databases were used, most commonly Lexicomp (9/20, 45%), Micromedex (8/20, 40%), Drugs@FDA (17/20, 85%), and DailyMed (13/20, 65%). Facts and Comparisons was the most commonly used compendia (8/20, 40%). Across the 6 attributes of interest, generic drug name was the most common keyword used. Respondents reported using more types of keywords when searching to identify the existence of PDDIs and determine their mechanism than when searching for the other 4 attributes (seriousness, consequences, management, and health outcomes). Regarding the types of evidence useful for evaluating a PDDI, clinical trials, case reports, and systematic reviews were considered relevant, while animal and in vitro data studies were not. CONCLUSIONS: This study suggests that drug interaction experts use various keyword strategies and various database and Web resources depending on the PDDI evidence they are seeking. Greater automation and standardization across search strategies could improve one's ability to identify PDDI evidence. Hence, future research focused on enhancing the existing search tools and designing recommended standards is needed.

Integrative medicine primary care: assessing the practice model through patients' experiences.

BACKGROUND: The University of Arizona Integrative Health Center (UAIHC) was an innovative integrative medicine (IM) adult primary care clinic in Phoenix, Arizona. UAIHC used a hybrid payment model to deliver comprehensive healthcare that includes conventional and complementary medical treatments. METHODS: Fidelity measures were collected to evaluate how well the IM care delivery process matched ideals for IM. Patient experiences are presented here. Patients visiting UAIHC on 1 of 10 randomly selected days between September 2013 and February 2015 were surveyed. Patients were asked about their experience with: holistic care; promotion of health, self-care, and well-being; relationship and communication with practitioners; and overall satisfaction. RESULTS: Eighty-three patients completed surveys. Based on patient-reported experiences, UAIHC delivered IM care as defined by the practice model. CONCLUSIONS: Patients received holistic care, established positive caring relationships with providers who promoted their self-care and well-being, and reported high overall satisfaction with UAIHC.

Recommendations for selecting drug-drug interactions for clinical decision support.

PURPOSE: Recommendations for including drug-drug interactions (DDIs) in clinical decision support (CDS) are presented. SUMMARY: A conference series was conducted to improve CDS for DDIs. A work group consisting of 20 experts in pharmacology, drug information, and CDS from academia, government agencies, health information vendors, and healthcare organizations was convened to address (1) the process to use for developing and maintaining a standard set of DDIs, (2) the information that should be included in a knowledge base of standard DDIs, (3) whether a list of contraindicated drug pairs can or should be established, and (4) how to more intelligently filter DDI alerts. We recommend a transparent, systematic, and evidence-driven process with graded recommendations by a consensus panel of experts and oversight by a national organization. We outline key DDI information needed to help guide clinician decision-making. We recommend judicious classification of DDIs as contraindicated and more research to identify methods to safely reduce repetitive and less-relevant alerts. CONCLUSION: An expert panel with a centralized organizer or convener should be established to develop and maintain a standard set of DDIs for CDS in the United States. The process should be evidence driven, transparent, and systematic, with feedback from multiple stakeholders for continuous improvement. The scope of the expert panel's work should be carefully managed to ensure that the process is sustainable. Support for research to improve DDI alerting in the future is also needed. Adoption of these steps may lead to consistent and clinically relevant content for interruptive DDIs, thus reducing alert fatigue and improving patient safety.

Accepting Medication Therapy Management Recommendations to Add ACEIs or ARBs in Diabetes Care.

BACKGROUND: National guidelines and initiatives have promoted the use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) for patients with diabetes. The University of Arizona Medication Management Center (UA-MMC) is contracted by Medicare health plans, pharmacy benefit managers (PBMs), and multiple commercial health insurance plans to provide medication therapy management (MTM) services for plan members. As part of the MTM program, recommendations have been made for those patients who may benefit from the addition of an ACEI/ARB. Although the intervention benefits and guidelines for using ACEIs/ARBs are clear, real-world evidence is needed to understand and potentially increase uptake of guideline interventions among eligible patients. OBJECTIVES: To (a) identify patient characteristics that predict acceptance of guideline recommendations to add ACEI/ARB medications to diabetic treatment via MTM services and (b) examine how well different case characteristics (i.e., patient age and sex, type and number of recommendation attempts, type of health care plan) predict the odds of adding ACEI/ARB medications to diabetic regimens when recommended through an MTM call center. METHODS: This was a retrospective analysis of secondary data provided by the UA-MMC. The de-identified national data included adult plan members with diabetes who the UA-MMC recommended adding an ACEI/ARB prescription based on 2012 national guidelines. The UA-MMC made recommendations by either patient letters, patient phone calls, physician faxes, or any combination thereof. We conducted a binary logistic regression analysis to assess the impact of case characteristics on the likelihood of accepting recommendations to add ACEI/ARB medications. The outcome variable was recommendation acceptance (yes/no), defined as new prescription claims for an ACEI/ARB within 120 days following the recommendation. Five predictor variables were assessed: (1) patient's age quartile; (2) method of communicating recommendations (letter, phone call, fax, or some combination thereof); (3) whether recommendations were made once or twice on separate dates; (4) patient's sex; and (5) type of health care plan. RESULTS: Recommendations were made for 31,495 members of health plans or PBMs that contracted with the UA-MMC. Patients' ages ranged from 19-90 (Mean =72.01; SD =10.21), with females comprising 56% of the sample. The recommendation to add ACEI/ARB medications was accepted for 14.5% (4,559) of patients. In most cases (73%), recommendations occurred via a letter to patients together with a fax to their providers. The fitted model, containing 3 predictor variables (age quartile, type of contact to communicate the recommendations, and whether recommendation contacts were made twice), was statistically significant, χ(2) (10; N = 31,495) = 112.82 (P < 0.001), indicating that the model was able to distinguish between those who did and did not accept UA-MMC's recommendations to add ACEI/ARB medications. The likelihood of recommendation acceptance decreased as patient age increased compared with patients in the first age quartile (ages 19-67; P ≤ 0.005 at all levels). Compared with sending only a provider fax, patients who received all 3 types of contact (provider fax with patient phone call and letter) were estimated to be 1.34 times more likely (34% increase) to have recommendation acceptance ( P = 0.004; 95% CI = 1.10-1.63). Similarly, patients who received only letters were also 1.32 times more likely (32% increase) than provider faxes alone to result in recommendation acceptance ( P = 0.003; 95% CI = 1.10-1.59). Patients for whom recommendations were made twice were less likely to have recommendation acceptance than for those contacted once, controlling for all other predictor variables in the model ( P < 0.001; OR = 0.77; 95% CI = 0.69-0.86). CONCLUSIONS: Recommendations to add an ACEI/ARB to diabetic regimens are more likely to be accepted for younger patients and those who receive recommendations through all 3 communication types (provider fax combined with patient phone call and letter) or just letters than provider faxes alone. Further research is needed to understand why prescribers are not accepting MTM recommendations.

Recommendations to improve the usability of drug-drug interaction clinical decision support alerts.

OBJECTIVE: To establish preferred strategies for presenting drug-drug interaction (DDI) clinical decision support alerts. MATERIALS AND METHODS: A DDI Clinical Decision Support Conference Series included a workgroup consisting of 24 clinical, usability, and informatics experts representing academia, health information technology (IT) vendors, healthcare organizations, and the Office of the National Coordinator for Health IT. Workgroup members met via web-based meetings 12 times from January 2013 to February 2014, and two in-person meetings to reach consensus on recommendations to improve decision support for DDIs. We addressed three key questions: (1) what, how, where, and when do we display DDI decision support? (2) should presentation of DDI decision support vary by clinicians? and (3) how should effectiveness of DDI decision support be measured? RESULTS: Our recommendations include the consistent use of terminology, visual cues, minimal text, formatting, content, and reporting standards to facilitate usability. All clinicians involved in the medication use process should be able to view DDI alerts and actions by other clinicians. Override rates are common but may not be a good measure of effectiveness. DISCUSSION: Seven core elements should be included with DDI decision support. DDI information should be presented to all clinicians. Finally, in their current form, override rates have limited capability to evaluate alert effectiveness. CONCLUSION: DDI clinical decision support alerts need major improvements. We provide recommendations for healthcare organizations and IT vendors to improve the clinician interface of DDI alerts, with the aim of reducing alert fatigue and improving patient safety.

Consensus recommendations for systematic evaluation of drug-drug interaction evidence for clinical decision support.

BACKGROUND: Healthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug-drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations. OBJECTIVE: The aim of this study was to provide recommendations for systematic evaluation of evidence for DDIs from the scientific literature, drug product labeling, and regulatory documents. METHODS: A conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 18 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar 12 times from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations. RESULTS: We developed expert consensus answers to the following three key questions. (i) What is the best approach to evaluate DDI evidence? (ii) What evidence is required for a DDI to be applicable to an entire class of drugs? (iii) How should a structured evaluation process be vetted and validated? CONCLUSION: Evidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug compendia and clinical decision support systems in which these recommendations are implemented should be able to provide higher-quality information about DDIs.

IMPACT--Integrative Medicine PrimAry Care Trial: protocol for a comparative effectiveness study of the clinical and cost outcomes of an integrative primary care clinic model.

BACKGROUND: Integrative medicine (IM) is a patient-centered, healing-oriented clinical paradigm that explicitly includes all appropriate therapeutic approaches whether they originate in conventional or complementary medicine (CM). While there is some evidence for the clinical and cost-effectiveness of IM practice models, the existing evidence base for IM depends largely on studies of individual CM therapies. This may in part be due to the methodological challenges inherent in evaluating a complex intervention (i.e., many interacting components applied flexibly and with tailoring) such as IM. METHODS/DESIGN: This study will use a combination of observational quantitative and qualitative methods to rigorously measure the health and healthcare utilization outcomes of the University of Arizona Integrative Health Center (UAIHC), an IM adult primary care clinic in Phoenix, Arizona. There are four groups of study participants. The primary group consists of clinic patients for whom clinical and cost outcomes will be tracked indicating the impact of the UAIHC clinic (n = 500). In addition to comparing outcomes pre/post clinic enrollment, where possible, these outcomes will be compared to those of two matched control groups, and for some self-report measures, to regional and national data. The second and third study groups consist of clinic patients (n = 180) and clinic personnel (n = 15-20) from whom fidelity data (i.e., data indicating the extent to which the IM practice model was implemented as planned) will be collected. These data will be analyzed to determine the exact nature of the intervention as implemented and to provide covariates to the outcomes analyses as the clinic evolves. The fourth group is made up of patients (n = 8) whose path through the clinic will be studied in detail using qualitative (periodic semi-structured interviews) methods. These data will be used to develop hypotheses regarding how the clinic works. DISCUSSION: The US health care system needs new models of care that are more patient-centered and empower patients to make positive lifestyle changes. These models have the potential to reduce the burden of chronic disease, lower the cost of healthcare, and offer a sustainable financial paradigm for our nation. This protocol has been designed to test whether the UAIHC can achieve this potential. TRIAL REGISTRATION: Clinical Trials.gov NCT01785485.

Assessment tool for pharmacy drug-drug interaction software.

OBJECTIVES: To assess the performance of pharmacy clinical decision support (CDS) systems for drug-drug interaction (DDI) detection and to identify approaches for improving the ability to recognize important DDIs. PRACTICE DESCRIPTION: Pharmacists rely on CDS systems to assist in the identification of DDIs, and research suggests that these systems perform suboptimally. The software evaluation tool described here may be used in all pharmacy settings that use electronic decision support to detect potential DDIs, including large and small community chain pharmacies, community independent pharmacies, hospital pharmacies, and governmental facility pharmacies. PRACTICE INNOVATION: A tool is provided to determine the ability of pharmacy CDS systems to identify established DDIs. It can be adapted to evaluate potential DDIs that reflect local practice patterns and patient safety priorities. Beyond assessing software performance, going through the evaluation processes creates the opportunity to evaluate inadequacies in policies, procedures, workflow, and training of all pharmacy staff relating to pharmacy information systems and DDIs. CONCLUSION: The DDI evaluation tool can be used to assess pharmacy information systems' ability to recognize relevant DDIs. Suggestions for improvement include determining whether the software allows for customization, creating standard policies for handling specific interactions, and ensuring that drug knowledge database updates occur frequently.

Pharmacists' awareness of clinical decision support in pharmacy information systems: an exploratory evaluation.

BACKGROUND: Clinical decision support (CDS), such as drug-drug interaction (DDI) and drug-allergy checking, has been used in pharmacy information systems for several decades; however, there has been limited research on CDS use by practicing pharmacists. OBJECTIVE: The purpose of this study was to document pharmacists' awareness of DDI and other medication-related CDS features available within pharmacy information systems. METHODS: Researchers conducted on-site interviews with pharmacists throughout the state of Arizona from December 2008 to November 2009 regarding their pharmacy information systems features. Pharmacists were asked to provide information about DDI and other medication-related decision support features of the pharmacy software at their practice site. Descriptive statistics were used to summarize interview responses. RESULTS: Sixty-one pharmacists from a variety of practice settings completed the interview. All respondents indicated that their pharmacy system provided drug-allergy and DDI alerts. Approximately 60% of the pharmacists reported that their DDI decision support systems included recommendations for managing drug interactions. Two-thirds of respondents reported that their pharmacy's computer system permitted the addition of medications from other pharmacies and/or over-the-counter products to a patient's profile. Approximately 40% of the pharmacists reported that some drugs entered into the pharmacy computer system were not included in (or linked to) the electronic DDI checking. Most pharmacists indicated the presence of other medication-related decision support features, such as drug-disease (78%), drug-age precautions (67%), and inappropriate dosage alerts (79%). However, fewer pharmacists reported more advanced functionality, such as laboratory recommendations (34%) and pediatric dosing (39%). CONCLUSION: Overall, pharmacists' awareness regarding the many decision support functionalities of their systems was limited. Based on the study findings, it appears that there are a number of limitations associated with currently available pharmacy decision support software. Further research is needed to formally evaluate pharmacist knowledge of pharmacy decision support software functionality. More formal training about software capabilities coupled with the addition of more advanced decision support features has the potential to improve pharmacists' use of these systems to make better clinical decisions and avoid preventable errors.

Ability of pharmacy clinical decision-support software to alert users about clinically important drug-drug interactions.

OBJECTIVE: Pharmacy clinical decision-support (CDS) software that contains drug-drug interaction (DDI) information may augment pharmacists' ability to detect clinically significant interactions. However, studies indicate these systems may miss some important interactions. The purpose of this study was to assess the performance of pharmacy CDS programs to detect clinically important DDIs. DESIGN: Researchers made on-site visits to 64 participating Arizona pharmacies between December 2008 and November 2009 to analyze the ability of pharmacy information systems and associated CDS to detect DDIs. Software evaluation was conducted to determine whether DDI alerts arose from prescription orders entered into the pharmacy computer systems for a standardized fictitious patient. The fictitious patient's orders consisted of 18 different medications including 19 drug pairs-13 of which were clinically significant DDIs, and six were non-interacting drug pairs. MEASUREMENTS: The sensitivity, specificity, positive predictive value, negative predictive value, and percentage of correct responses were measured for each of the pharmacy CDS systems. RESULTS: Only 18 (28%) of the 64 pharmacies correctly identified eligible interactions and non-interactions. The median percentage of correct DDI responses was 89% (range 47-100%) for participating pharmacies. The median sensitivity to detect well-established interactions was 0.85 (range 0.23-1.0); median specificity was 1.0 (range 0.83-1.0); median positive predictive value was 1.0 (range 0.88-1.0); and median negative predictive value was 0.75 (range 0.38-1.0). CONCLUSIONS: These study results indicate that many pharmacy clinical decision-support systems perform less than optimally with respect to identifying well-known, clinically relevant interactions. Comprehensive system improvements regarding the manner in which pharmacy information systems identify potential DDIs are warranted.

Contemporary pharmacologic treatments for spasticity of the upper limb after stroke: a systematic review.

BACKGROUND: Muscle spasticity after stroke may be painful and severe and may restrict the patient's ability to perform routine daily tasks, particularly when the affected muscles are in the upper limbs. Treatments targeted at reducing this spasticity have evolved over time. OBJECTIVE: This was a systematic review of recent studies focusing on contemporary pharmacologic therapies for upper limb spasticity after stroke. METHODS: MEDLINE, EMBASE, and the Cochrane Controlled Trials Register were searched for clinical trials published in English from January 1995 to July 2010 using search terms that included spasticity, stroke, hemiplegia, phenol, baclofen, tizanidine, dantrolene, benzodiazepine, and botulinum toxin. The level of evidence of the identified publications was assessed using the Oxford Centre for Evidence-Based Medicine criteria. RESULTS: A total of 113 potentially relevant articles were identified by the search; of these, 54 studies were included in the review (23 randomized controlled trials [RCTs] and 31 open-label, nonrandomized, or observational studies). Of these, 51 involved treatment with botulinum toxin (BTX). All studies assessed spasticity; some also assessed additional outcomes, such as pain, disability, and functional status. Thirty-eight clinical trials reported a significant reduction in spasticity with BTX, either compared with baseline or with placebo (P < 0.05). A head-to-head comparison found a significant reduction in spasticity with BTX injections compared with oral tizanidine (TZD) (P < 0.001). Two studies of intrathecal baclofen (ITB) reported significant reductions in upper limb spasticity after 12 months of treatment, and 1 study of tizanidine reported significant reductions in upper limb spasticity after 16 weeks of treatment (all, P < 0.001). General or local weakness, injection-site pain, and fatigue were the most frequently reported adverse events with BTX type A, and dry mouth was the most frequently reported adverse event with BTX type B. No serious or life-threatening adverse events were reported in any trial of BTX. CONCLUSIONS: The 54 studies included in this systematic review of treatments for upper limb spasticity after stroke measured multiple outcomes using a variety of instruments. Fifty-one studies focused on treatment with a BTX formulation. BTX appeared to be an effective and well-tolerated focal treatment for reducing tonicity in patients with upper limb spasticity after stroke, supporting current guideline recommendations.

Development of computerized alerts with management strategies for 25 serious drug-drug interactions.

PURPOSE: The development of computerized alerts with management strategies for 25 drug-drug interactions (DDIs) is described. SUMMARY: To ensure that clinicians, when confronted with any of 25 serious DDIs in the ambulatory care setting, can avoid these DDIs while treating patients with appropriate medications, management strategies were developed using a consensus approach. Several methods were used to ensure that the recommended alternatives were truly safe. Four well-established drug-interaction compendia were screened, and any potential alternative agent that was listed as having an interaction (moderate or serious in nature) was excluded from the list of alternative agents. Case reports, case series, and clinical studies that focused on the alternative combinations were reviewed to determine if the alternative posed interaction risks. If an interaction for the alternative combination had not been identified in the compendia or in the literature search, other potential mechanisms for drug interactions were explored such as alterations in absorption, distribution, metabolism, or excretion. Pharmacology and therapeutics textbooks and other drug information sources also served as resources. In general, the strategies included alternatives to both medications, changing dosage or increasing monitoring of one of the agents, situations where one of the medications had no alternative but alternatives were available for the other medication, and alternative methods of birth control. In some situations the two drugs were contraindicated, while in others the two drugs should be avoided if at all possible and alternatives used. CONCLUSION: Consensus-based management strategies for 25 serious DDIs were developed for inclusion in computerized alert messages.

Reasons provided by prescribers when overriding drug-drug interaction alerts.

OBJECTIVES: To investigate prescribers' rationales for overriding drug-drug interaction (DDI) alerts and to determine whether these reasons were helpful to pharmacists as a part of prescription order verification. STUDY DESIGN: An observational retrospective database analysis was conducted using override reasons derived from a computerized system at 6 Veterans Affairs medical centers. METHODS: Data on DDI alerts (for interactions designated as "critical" and "significant") were obtained from ambulatory care pharmacy records from July 1, 2003, to June 30, 2004. Prescribers' reasons for overriding alerts were organized into 14 categories and were then rated as clinically useful or not to the pharmacist in the assessment of potential patient harm. RESULTS: Of 291,890 overrides identified, 72% were for critical DDIs. Across the Veterans Affairs medical centers, only 20% of the override reasons for critical DDI alerts were rated as clinically useful for order verification. Despite a mandatory override reason for critical DDI alerts, 53% of the responses were "no reason provided." The top response categories for critical and significant DDI alerts were "no reason provided," "patient has been taking combination," and "patient being monitored." CONCLUSIONS: When given the opportunity to provide a reason for overriding a DDI alert, prescribers rarely enter clinical justifications that are useful to order verification pharmacists. This brings into question how computerized physician order entry systems should be designed.