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Integrating modern machine learning and clinical decision-making has great promise for mitigating healthcare's increasing cost and complexity. We introduce the Enhanced Transformer for Health Outcome Simulation (ETHOS), a novel application of the transformer deep-learning architecture for analyzing high-dimensional, heterogeneous, and episodic health data. ETHOS is trained using Patient Health Timelines (PHTs)—detailed, tokenized records of health events—to predict future health trajectories, leveraging a zero-shot learning approach. ETHOS represents a significant advancement in foundation model development for healthcare analytics, eliminating the need for labeled data and model fine-tuning. Its ability to simulate various treatment pathways and consider patient-specific factors positions ETHOS as a tool for care optimization and addressing biases in healthcare delivery. Future developments will expand ETHOS' capabilities to incorporate a wider range of data types and data sources. Our work demonstrates a pathway toward accelerated AI development and deployment in healthcare. [ABSTRACT FROM AUTHOR]

Background: Unmet social needs (SNs) often coexist in distinct patterns within specific population subgroups, yet these patterns are understudied. Objective: To identify patterns of social needs (PSNs) and characterize their associations with health-related quality-of-life (HRQoL) and healthcare utilization (HCU). Design: Observational study using data on SNs screening, HRQoL (i.e., low mental and physical health), and 90-day HCU (i.e., emergency visits and hospital admission). Among patients with any SNs, latent class analysis was conducted to identify unique PSNs. For all patients and by race and age subgroups, compared with no SNs, we calculated the risks of poor HRQoL and time to first HCU following SNs screening for each PSN. Patients: Adult patients undergoing SNs screening at the Mass General Brigham healthcare system in Massachusetts, United States, between March 2018 and January 2023. Main Measures: SNs included: education, employment, family care, food, housing, medication, transportation, and ability to pay for household utilities. HRQoL was assessed using the Patient-Reported Outcomes Measurement Information System Global-10. Key Results: Six unique PSNs were identified: "high number of social needs," "food and utility access," "employment needs," "interested in education," "housing instability," and "transportation barriers." In 14,230 patients with HRQoL data, PSNs increased the risks of poor mental health, with risk ratios ranging from 1.07(95%CI:1.01–1.13) to 1.80(95%CI:1.74–1.86). Analysis of poor physical health yielded similar findings, except that the "interested in education" showed a mild protective effect (0.97[95%CI:0.94–1.00]). In 105,110 patients, PSNs increased the risk of 90-day HCU, with hazard ratios ranging from 1.09(95%CI:0.99–1.21) to 1.70(95%CI:1.52–1.90). Findings were generally consistent in subgroup analyses by race and age. Conclusions: Certain SNs coexist in distinct patterns and result in poorer HRQoL and more HCU. Understanding PSNs allows policymakers, public health practitioners, and social workers to identify at-risk patients and implement integrated, system-wide, and community-based interventions. [ABSTRACT FROM AUTHOR]

Introduction: Infants in the neonatal intensive care unit (NICU) are among the most vulnerable patient populations and medication errors are a significant source of risk and harm to neonates. Smart infusion pumps have been implemented to support the safe medication administration process; however, the effect of using smart infusion pumps on medication safety in the NICU is still unclear. Methods: We conducted an observational study with a prospective point-prevalence approach to investigate intravenous (IV) medication administration errors in the NICU at one academic medical center in the USA. Observations were conducted in 48 days in a 3-month data collection period in 2019. Results: We observed a total of 441 patients with 905 IV medication administrations during the data collection period. The total number of errors was 130 (14.4 per 100 administrations). Of these, the most frequent errors were selecting the wrong drug library entry (5.3 per 100 administrations), unauthorized medication (0.7 per 100 administrations), and wrong dose (0.6 per 100 administrations). Sixty-eight errors (7.5 per 100 administrations) were unlikely to cause harm despite reaching the patient (category C errors), while the rest did not reach the patient. Conclusion: We identified the medication errors, which was unique to NICU populations, but no harm to the patients were identified. Most errors occurred due to a lack of compliance of using smart pump technology; therefore, potential exists to maximize safety related to medication administration practices in the NICU through hospital policy change and increasing adherence to appropriate use of smart pump technology. [ABSTRACT FROM AUTHOR]

Many areas of medicine would benefit from deeper, more accurate phenotyping, but there are limited approaches for phenotyping using clinical notes without substantial annotated data. Large language models (LLMs) have demonstrated immense potential to adapt to novel tasks with no additional training by specifying task-specific instructions. Here we report the performance of a publicly available LLM, Flan-T5, in phenotyping patients with postpartum hemorrhage (PPH) using discharge notes from electronic health records (n = 271,081). The language model achieves strong performance in extracting 24 granular concepts associated with PPH. Identifying these granular concepts accurately allows the development of interpretable, complex phenotypes and subtypes. The Flan-T5 model achieves high fidelity in phenotyping PPH (positive predictive value of 0.95), identifying 47% more patients with this complication compared to the current standard of using claims codes. This LLM pipeline can be used reliably for subtyping PPH and outperforms a claims-based approach on the three most common PPH subtypes associated with uterine atony, abnormal placentation, and obstetric trauma. The advantage of this approach to subtyping is its interpretability, as each concept contributing to the subtype determination can be evaluated. Moreover, as definitions may change over time due to new guidelines, using granular concepts to create complex phenotypes enables prompt and efficient updating of the algorithm. Using this language modelling approach enables rapid phenotyping without the need for any manually annotated training data across multiple clinical use cases. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has the potential to transform care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. AI will be critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. There is also universal concern about the ability to monitor health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change. The Future of Health (FOH), an international community of senior health care leaders, collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise around this topic. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers across the globe that FOH members identified as important for fully realizing AI's potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI. [ABSTRACT FROM AUTHOR]

This letter published in the Journal of General Internal Medicine discusses a study that examined the association between primary care physicians' electronic inbox activity and their patients' likelihood to recommend them. The study found that female PCPs receive more messages than male PCPs and that there are differences in patients' expectations and interactions with female physicians. The results showed that greater electronic inbox activity was associated with increased odds of a higher likelihood to recommend score, particularly for female PCPs. However, longer turnaround time was associated with decreased odds of a high likelihood to recommend score. These findings suggest the importance of managing inbox work and compensating PCPs for non-visit work. [Extracted from the article]

Background: The burden of clinical documentation in electronic health records (EHRs) has been associated with physician burnout. Numerous tools (e.g., note templates and dictation services) exist to ease documentation burden, but little evidence exists regarding how physicians use these tools in combination and the degree to which these strategies correlate with reduced time spent on documentation. Objective: To characterize EHR note composition strategies, how these strategies differ in time spent on notes and the EHR, and their distribution across specialty types. Design: Secondary analysis of physician-level measures of note composition and EHR use derived from Epic Systems' Signal data warehouse. We used k-means clustering to identify documentation strategies, and ordinary least squares regression to analyze the relationship between documentation strategies and physician time spent in the EHR, on notes, and outside scheduled hours. Participants: A total of 215,207 US-based ambulatory physicians using the Epic EHR between September 2020 and May 2021. Main Measures: Percent of note text derived from each of five documentation tools: SmartTools, copy/paste, manual text, NoteWriter, and voice recognition and transcription; average total and after-hours EHR time per visit; average time on notes per visit. Key Results: Six distinct note composition strategies emerged in cluster analyses. The most common strategy was predominant SmartTools use (n=89,718). In adjusted analyses, physicians using primarily transcription and dictation (n=15,928) spent less time on notes than physicians with predominant Smart Tool use. (b=−1.30, 95% CI=−1.62, −0.99, p<0.001; average 4.8 min per visit), while those using mostly copy/paste (n=23,426) spent more time on notes (b=2.38, 95% CI=1.92, 2.84, p<0.001; average 13.1 min per visit). Conclusions: Physicians' note composition strategies have implications for both time in notes and after-hours EHR use, suggesting that how physicians use EHR-based documentation tools can be a key lever for institutions investing in EHR tools and training to reduce documentation time and alleviate EHR-associated burden. [ABSTRACT FROM AUTHOR]

Leveraging the science of human factors which is an applied field of engineering to understand user needs and use this information to inform the implementation and use of the new EHR can ease the burdens of EHR transitions. Any of these EHR vendor products can represent a reasonable choice depending on the size and complexity of the hospital. Electronic Health Record Transitions - How to Make Them Work. [Extracted from the article]

Background: The first surge of the COVID-19 pandemic entirely altered healthcare delivery. Whether this also altered the receipt of high- and low-value care is unknown.Objective: To test the association between the April through June 2020 surge of COVID-19 and various high- and low-value care measures to determine how the delivery of care changed.Design: Difference in differences analysis, examining the difference in quality measures between the April through June 2020 surge quarter and the January through March 2020 quarter with the same 2 quarters' difference the year prior.Participants: Adults in the MarketScan® Commercial Database and Medicare Supplemental Database.Main Measures: Fifteen low-value and 16 high-value quality measures aggregated into 8 clinical quality composites (4 of these low-value).Key Results: We analyzed 9,352,569 adults. Mean age was 44 years (SD, 15.03), 52% were female, and 75% were employed. Receipt of nearly every type of low-value care decreased during the surge. For example, low-value cancer screening decreased 0.86% (95% CI, -1.03 to -0.69). Use of opioid medications for back and neck pain (DiD +0.94 [95% CI, +0.82 to +1.07]) and use of opioid medications for headache (DiD +0.38 [95% CI, 0.07 to 0.69]) were the only two measures to increase. Nearly all high-value care measures also decreased. For example, high-value diabetes care decreased 9.75% (95% CI, -10.79 to -8.71).Conclusions: The first COVID-19 surge was associated with receipt of less low-value care and substantially less high-value care for most measures, with the notable exception of increases in low-value opioid use. [ABSTRACT FROM AUTHOR]

Pharmacovigilance improves patient safety by detecting and preventing adverse drug events. However, challenges exist that limit adverse drug event detection, resulting in many adverse drug events being underreported or inaccurately reported. One challenge includes having access to large data sets from various sources including electronic health records and wearable medical devices. Artificial intelligence, including machine learning methods, such as natural language processing and deep learning, can detect and extract information about adverse drug events, thus automating the pharmacovigilance process and improving the surveillance of known and documented adverse drug events. In addition, with the increased demand for telehealth services, for managing both acute and chronic diseases, artificial intelligence methods can play a role in detecting and preventing adverse drug events. In this review, we discuss two use cases of how artificial intelligence methods may be useful to improve the quality of pharmacovigilance and the role of artificial intelligence in telehealth practices. [ABSTRACT FROM AUTHOR]

An abundant and growing supply of digital health applications (apps) exists in the commercial tech-sector, which can be bewildering for clinicians, patients, and payers. A growing challenge for the health care system is therefore to facilitate the identification of safe and effective apps for health care practitioners and patients to generate the most health benefit as well as guide payer coverage decisions. Nearly all developed countries are attempting to define policy frameworks to improve decision-making, patient care, and health outcomes in this context. This study compares the national policy approaches currently in development/use for health apps in nine countries. We used secondary data, combined with a detailed review of policy and regulatory documents, and interviews with key individuals and experts in the field of digital health policy to collect data about implemented and planned policies and initiatives. We found that most approaches aim for centralized pipelines for health app approvals, although some countries are adding decentralized elements. While the countries studied are taking diverse paths, there is nevertheless broad, international convergence in terms of requirements in the areas of transparency, health content, interoperability, and privacy and security. The sheer number of apps on the market in most countries represents a challenge for clinicians and patients. Our analyses of the relevant policies identified challenges in areas such as reimbursement, safety, and privacy and suggest that more regulatory work is needed in the areas of operationalization, implementation and international transferability of approvals. Cross-national efforts are needed around regulation and for countries to realize the benefits of these technologies. [ABSTRACT FROM AUTHOR]

Delivery of serious illness communication (SIC) is necessary to ensure that all seriously ill patients receive goal-concordant care. However, the current SIC delivery process contains barriers that prevent the delivery of timely and effective SIC. In this paper, we describe the current bottlenecks of the traditional SIC workflow and explore how a hybrid artificial intelligence-human workflow may improve the efficiency and effectiveness of SIC delivery in busy practice settings. [ABSTRACT FROM AUTHOR]

The integration of advanced analytics and artificial intelligence (AI) technologies into the practice of medicine holds much promise. Yet, the opportunity to leverage these tools carries with it an equal responsibility to ensure that principles of equity are incorporated into their implementation and use. Without such efforts, tools will potentially reflect the myriad of ways in which data, algorithmic, and analytic biases can be produced, with the potential to widen inequities by race, ethnicity, gender, and other sociodemographic factors implicated in disparate health outcomes. We propose a set of strategic assertions to examine before, during, and after adoption of these technologies in order to facilitate healthcare equity across all patient population groups. The purpose is to enable generalists to promote engagement with technology companies and co-create, promote, or support innovation and insights that can potentially inform decision-making and health care equity. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics. [ABSTRACT FROM AUTHOR]

Introduction: Medication organizations across the USA have adopted electronic health records, and one of the most anticipated benefits of these was improved medication safety, but alert fatigue has been a major issue.Objective: We compared the appropriateness of medication-related clinical decision support alerts triggered by two commercial applications: EPIC and Seegnal's platform.Methods: This was a retrospective comparison of two commercial applications. We provided Seegnal with deidentified inpatient, outpatient, and inpatient genetic electronic medical record (EMR)-extracted datasets for 657, 2731, and 413 patients, respectively. Seegnal then provided the alerts that would have triggered, which we compared with those triggered by EPIC in clinical care. A random sample of the alerts triggered were reviewed for appropriateness, and the positive predictive value (PPV) and negative predictive value (NPV) were calculated. We also reviewed all the inpatient and outpatient charts for patients within our cohort who were receiving ten or more concomitant medications with alerts we found to be appropriate to assess whether any adverse events had occurred and whether Seegnal's platform could have prevented them.Results: Results from EPIC and the Seegnal platform were compared based on alert load, PPV, NPV, and potential adverse events. Overall, compared with EPIC, the Seegnal platform triggered fewer alerts in the inpatient (1697 vs. 27,540), outpatient (2341 vs. 35,134), and inpatient genetic (1493 vs. 20,975) cohorts. The Seegnal platform had higher specificity in the inpatient (99 vs. 0.3%; p < 0.0001), outpatient (99 vs. 0.3%; p < 0.0001), and inpatient genetic (97.9 vs. 1.2%; p < 0.0001) groups and higher sensitivity in the inpatient (100 vs. 68.8%; p < 0.0001) and outpatient (88.6 vs.78.3%; p < 0.0001) groups but not in the inpatient genetic cohort (81 vs. 78.5%; p = 0.11). We identified 16 adverse events that occurred in the inpatient setting, 11 (69%) of which potentially could have been prevented with the Seegnal platform.Conclusions: Overall, the Seegnal platform triggered 94% fewer alerts than EPIC in the inpatient setting and 93% fewer in the outpatient setting, with much higher sensitivity and specificity. This application could substantially reduce alert fatigue and improve medication safety at the same time. [ABSTRACT FROM AUTHOR]

Background: Self-rated health is a strong predictor of mortality and morbidity. Machine learning techniques may provide insights into which of the multifaceted contributors to self-rated health are key drivers in diverse groups.Objective: We used machine learning algorithms to predict self-rated health in diverse groups in the Behavioral Risk Factor Surveillance System (BRFSS), to understand how machine learning algorithms might be used explicitly to examine drivers of self-rated health in diverse populations.Design: We applied three common machine learning algorithms to predict self-rated health in the 2017 BRFSS survey, stratified by age, race/ethnicity, and sex. We replicated our process in the 2016 BRFSS survey.Participants: We analyzed data from 449,492 adult participants of the 2017 BRFSS survey.Main Measures: We examined area under the curve (AUC) statistics to examine model fit within each group. We used traditional logistic regression to predict self-rated health associated with features identified by machine learning models.Key Results: Each algorithm, regularized logistic regression (AUC: 0.81), random forest (AUC: 0.80), and support vector machine (AUC: 0.81), provided good model fit in the BRFSS. Predictors of self-rated health were similar by sex and race/ethnicity but differed by age. Socioeconomic features were prominent predictors of self-rated health in mid-life age groups. Income [OR: 1.70 (95% CI: 1.62-1.80)], education [OR: 2.02 (95% CI: 1.89, 2.16)], physical activity [OR: 1.52 (95% CI: 1.46-1.58)], depression [OR: 0.66 (95% CI: 0.63-0.68)], difficulty concentrating [OR: 0.62 (95% CI: 0.58-0.66)], and hypertension [OR: 0.59 (95% CI: 0.57-0.61)] all predicted the odds of excellent or very good self-rated health.Conclusions: Our analysis of BRFSS data show social determinants of health are prominent predictors of self-rated health in mid-life. Our work may demonstrate promising practices for using machine learning to advance health equity. [ABSTRACT FROM AUTHOR]

Introduction: Medication-related harm represents a significant issue for patient safety and quality of care. One strategy to avoid preventable adverse drug events is to utilize patient-specific factors such as pharmacogenomics (PGx) to individualize therapy.Objective: We measured the number of patients enrolled in a health-system biobank with actionable PGx results who received relevant medications and assessed the incidence of adverse drug events (ADEs) that might have been prevented had the PGx results been used to inform prescribing.Methods: Patients with actionable PGx results in the following four genes with Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines were identified: HLA-A*31:01, HLA-B*15:02, TPMT, and VKORC1. The patients who received interacting medications (carbamazepine, oxcarbazepine, thiopurines, or warfarin) were identified, and electronic health records were reviewed to determine the incidence of potentially preventable ADEs.Results: Of 36,424 patients with PGx results, 2327 (6.4%) were HLA-A*31:01 positive; 3543 (9.7%) were HLA-B*15:02 positive; 2893 (7.9%) were TPMT intermediate metabolizers; and 4249 (11.7%) were homozygous for the VKORC1 c.1639 G>A variant. Among patients positive for one of the HLA variants who received carbamazepine or oxcarbazepine (n = 92), four (4.3%) experienced a rash that warranted drug discontinuation. Among the TPMT intermediate metabolizers who received a thiopurine (n = 56), 11 (19.6%) experienced severe myelosuppression that warranted drug discontinuation. Among patients homozygous for the VKORC1 c.1639 G>A variant who received warfarin (n = 379), 85 (22.4%) experienced active bleeding and/or international normalized ratio (INR) > 5 that warranted drug discontinuation or dose reduction.Conclusion: Patients with actionable PGx results from a health-system biobank who received relevant medications experienced predictable ADEs. These ADEs may have been prevented if the patients' PGx results were available in the electronic health record with clinical decision support prior to prescribing. [ABSTRACT FROM AUTHOR]

Finally, health providers and health systems can help accelerate broadband deployment in low-resource communities by supporting policies that reduce financial, bureaucratic, and structural barriers to the expansion of required infrastructure, such as digital redlining of less economically profitable neighborhoods [[2], [10], [13]]. Health providers can advocate for federal mandates like the Culturally and Linguistically Appropriate Service Standards to formalize guidance on promoting digital health equity, such as by expanding the availability of interpreter services for telehealth visits [[23]]. Conclusion The COVID-19 pandemic has highlighted that digital access is now a social determinant of health and a prerequisite for access to both COVID-related and non-COVID care [[1], [19]]. As telemedicine use for healthcare delivery during the COVID-19 pandemic has grown, access to technology has emerged as a fundamental determinant of access to care. [Extracted from the article]

Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care. Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation, and diagnostic errors. The objective of this scoping review was to summarize the relevant literature and evaluate the potential of AI to improve patient safety in these eight harm domains. A structured search was used to query MEDLINE for relevant articles. The scoping review identified studies that described the application of AI for prediction, prevention, or early detection of adverse events in each of the harm domains. The AI literature was narratively synthesized for each domain, and findings were considered in the context of incidence, cost, and preventability to make projections about the likelihood of AI improving safety. Three-hundred and ninety-two studies were included in the scoping review. The literature provided numerous examples of how AI has been applied within each of the eight harm domains using various techniques. The most common novel data were collected using different types of sensing technologies: vital sign monitoring, wearables, pressure sensors, and computer vision. There are significant opportunities to leverage AI and novel data sources to reduce the frequency of harm across all domains. We expect AI to have the greatest impact in areas where current strategies are not effective, and integration and complex analysis of novel, unstructured data are necessary to make accurate predictions; this applies specifically to adverse drug events, decompensation, and diagnostic errors. [ABSTRACT FROM AUTHOR]

Mobile health applications ("apps") have rapidly proliferated, yet their ability to improve outcomes for patients remains unclear. A validated tool that addresses apps' potentially important dimensions has not been available to patients and clinicians. The objective of this study was to develop and preliminarily assess a usable, valid, and open-source rating tool to objectively measure the risks and benefits of health apps. We accomplished this by using a Delphi process, where we constructed an app rating tool called THESIS that could promote informed app selection. We used a systematic process to select chronic disease apps with ≥4 stars and <4-stars and then rated them with THESIS to examine the tool's interrater reliability and internal consistency. We rated 211 apps, finding they performed fair overall (3.02 out of 5 [95% CI, 2.96–3.09]), but especially poorly for privacy/security (2.21 out of 5 [95% CI, 2.11–2.32]), interoperability (1.75 [95% CI, 1.59–1.91]), and availability in multiple languages (1.43 out of 5 [95% CI, 1.30–1.56]). Ratings using THESIS had fair interrater reliability (κ = 0.3–0.6) and excellent scale reliability (ɑ = 0.85). Correlation with traditional star ratings was low (r = 0.24), suggesting THESIS captures issues beyond general user acceptance. Preliminary testing of THESIS suggests apps that serve patients with chronic disease could perform much better, particularly in privacy/security and interoperability. THESIS warrants further testing and may guide software and policymakers to further improve app performance, so apps can more consistently improve patient outcomes. [ABSTRACT FROM AUTHOR]

The digital health industry has grown rapidly in the past decade. There will be few future aspects of healthcare untouched by digital health. Thus, the current status of the industry, the implications of companies' directions and clinical focus, and their external funding are increasingly relevant to healthcare policy, regulation, research, and all healthcare stakeholders. Yet, little is known about the degree to which the digital health industry has focused on the key domains in the health continuum, including prevention, detection, and management. We performed a cross-sectional study of a US digital health industry cohort that received publicly disclosed funding from 2011–2018. We assessed the number of companies; respective funding within each part of the health continuum; and products and services by technology type, clinical indication, purchasers, and end users. In this emerging industry, most companies focused on management of disease and the minority on prevention or detection. This asymmetry, which is similar to the traditional healthcare system, represents an opportunity to focus on earlier parts of the health continuum. Patients were a common purchaser of all products, but especially prevention-focused digital health products, implying a large unmet need not yet served by the traditional healthcare system. [ABSTRACT FROM AUTHOR]

Fueled by advances in technology, increased access to smartphones, and capital investment, the number of available health "apps" has exploded in recent years. Patients use their smartphones for many things, but not as much as they might for health, especially for managing their chronic conditions. Moreover, while significant work is ongoing to develop, validate, and evaluate these apps, it is less clear how to effectively disseminate apps into routine clinical practice. We propose a framework for prescribing apps and outline the key issues that need to be addressed to enable app dissemination in clinical care. This includes: education and awareness, creating digital formularies, workflow and EHR integration, payment models, and patient/provider support. As work in digital health continues to expand, integrating health apps into clinical care delivery will be critical if digital health is to achieve its potential. [ABSTRACT FROM AUTHOR]

Introduction: Intravenous medication administration is widely reported to be error prone. Technologies such as smart pumps have been introduced with a view to reducing these errors. An international comparison could provide evidence of their effectiveness, including consideration of contextual factors such as regulatory systems and local cultures.Objectives: The aim of this study was to investigate similarities and differences in practices and error types involving intravenous medication administration in the United States and England, and summarise methodological differences necessary to perform these parallel studies.Methods: We drew on findings of separate point prevalence studies conducted across hospitals in each country. In these, we compared what was being administered at the time of observation with the prescription and relevant policies, errors were classified by type and severity, and proportions of infusions featuring each error type were calculated. We also reviewed what adaptations to the US protocol were needed for England. Authors independently reviewed findings from both studies and proposed themes for comparison. In online meetings, each country's research team clarified assumptions and explained their findings.Results: Key themes included commonalities and contrasts in methods, findings, practices and policies. Although US sites made greater use of smart infusion devices, and had more precisely defined requirements around infusion device use, patterns of errors were similar. Differences among clinical contexts within each country were as marked as differences across countries. Regulatory and quality control systems shape practices, but causal relationships are complex.Conclusion: Infusion administration is a complex adaptive system with multiple interacting agents (professionals, patients, software systems, etc.) that respond in rich ways to their environments; safety depends on complex, interrelated factors. [ABSTRACT FROM AUTHOR]

Background: Primary care providers (PCPs) often take the lead role in caring for patients with overweight and obesity; however, few PCPs counsel patients about weight loss. Online weight management programs that are integrated within primary care may help address this gap in care.Objective: To identify perceptions of and experience with online weight management programs in general and with a proposed online program, to identify barriers to use, and to improve the design and content of our intervention, which included an online program plus population health management (PHM) support from primary care practices.Design: A mixed qualitative methods study including three patient focus groups and seven semi-structured interviews with healthcare providers.Participants: A total of 13 adult patients (age range, 20-70) with body mass index (BMI) 27-35 kg/m2 attended the focus groups. In-person semi-structured interviews were conducted with seven healthcare providers (three PCPs, two population health managers, one primary care nurse, and one registered dietitian).Main Measures: We developed and used semi-structured focus groups and interview guides. The focus group and interviews were recorded and transcribed. Using grounded theory, we analyzed the transcripts to identify and extract common concepts and themes.Key Results: Although patients and healthcare providers expressed positive opinions about online weight management programs, few patients had experience with them, and providers stated that such programs are not being widely implemented in primary care settings. Some participants highlighted the flexibility and low cost as strengths of online weight management tools compared with in-person programs. All participants had favorable opinions about our proposed intervention and were overwhelmingly positive about the combination of an online program and PHM support.Conclusions: This study highlights the potential value of online weight management programs and PHM support in primary care.Clinical Trials Registration: NCT02656693. [ABSTRACT FROM AUTHOR]

Introduction: Medication-related clinical decision support (CDS) alerts have been shown to be effective at reducing adverse drug events (ADEs). However, these alerts are frequently overridden, with limited data linking these overrides to harm. Dose-range checking alerts are a type of CDS alert that could have a significant impact on morbidity and mortality, especially in the intensive care unit (ICU) setting. Methods: We performed a single-center, prospective, observational study of adult ICUs from September 2016 to April 2017. Targeted overridden alerts were triggered when doses greater than or equal to 5% over the maximum dose were prescribed. The primary outcome was the appropriateness of the override, determined by two independent reviewers, using pre-specified criteria formulated by a multidisciplinary group. Overrides which resulted in medication administration were then evaluated for ADEs by chart review. Results: The override rate of high dose-range alerts in the ICU was 93.0% (total n = 1525) during the study period. A total of 1418 overridden alerts from 755 unique patients were evaluated for appropriateness (appropriateness rate 88.8%). The most common medication associated with high dose-range alerts was insulin regular infusion (n = 262, 18.5%). The rates of ADEs for the appropriately and inappropriately overridden alerts per 100 overridden alerts were 1.3 and 5.0, respectively (p < 0.001). Conclusions: Overriding high dose-range CDS alerts was found to be common and often appropriate, suggesting that more intelligent dose checking is needed. Some alerts were clearly inappropriately presented to the provider. Inappropriate overrides were associated with an increased risk of ADEs, compared to appropriately overridden alerts. [ABSTRACT FROM AUTHOR]

Background: Medication adverse events are important and common yet are often not identified by clinicians. We evaluated an automated telephone surveillance system coupled with transfer to a live pharmacist to screen potentially drug-related symptoms after newly starting medications for four common primary care conditions: hypertension, diabetes, depression, and insomnia.Methods: Cluster randomized trial with automated calls to eligible patients at 1 and 4 months after starting target drugs from intervention primary care clinics compared to propensity-matched patients from control clinics. Primary and secondary outcomes were physician documentation of any adverse effects associated with newly prescribed target medication, and whether the medication was discontinued and, if yes, whether the reason for stopping was an adverse effect.Results: Of 4876 eligible intervention clinic patients who were contacted using automated calls, 776 (15.1%) responded and participated in the automated call. Based on positive symptom responses or request to speak to a pharmacist, 320 patients were transferred to the pharmacist and discussed 1021 potentially drug-related symptoms. Of these, 188 (18.5%) were assessed as probably and 479 (47.1%) as possibly related to the medication. Compared to a propensity-matched cohort of control clinic patients, intervention patients were significantly more likely to have adverse effects documented in the medical record by a physician (277 vs. 164 adverse effects, p < 0.0001, and 177 vs. 122 patients discontinued with documented adverse effects, p < 0.0001).Discussion: Systematic automated telephone outreach monitoring coupled with real-time phone referral to a pharmacist identified a substantial number of previously unidentified potentially drug-related symptoms, many of which were validated as probably or possibly related to the drug by the pharmacist or their physicians. Multiple challenges were encountered using the interactive voice response (IVR) automated calling system, suggesting that other approaches may need to be considered and evaluated.Trial Registration: ClinicalTrials.gov : NCT02087293. [ABSTRACT FROM AUTHOR]

Background: Drug-drug interaction (DDI) alerts in electronic health records (EHRs) can help prevent adverse drug events, but such alerts are frequently overridden, raising concerns about their clinical usefulness and contribution to alert fatigue.Objective: To study the effect of conversion to a commercial EHR on DDI alert and acceptance rates.Design: Two before-and-after studies.Participants: 3277 clinicians who received a DDI alert in the outpatient setting.Intervention: Introduction of a new, commercial EHR and subsequent adjustment of DDI alerting criteria.Main Measures: Alert burden and proportion of alerts accepted.Key Results: Overall interruptive DDI alert burden increased by a factor of 6 from the legacy EHR to the commercial EHR. The acceptance rate for the most severe alerts fell from 100 to 8.4%, and from 29.3 to 7.5% for medium severity alerts (P < 0.001). After disabling the least severe alerts, total DDI alert burden fell by 50.5%, and acceptance of Tier 1 alerts rose from 9.1 to 12.7% (P < 0.01).Conclusions: Changing from a highly tailored DDI alerting system to a more general one as part of an EHR conversion decreased acceptance of DDI alerts and increased alert burden on users. The decrease in acceptance rates cannot be fully explained by differences in the clinical knowledge base, nor can it be fully explained by alert fatigue associated with increased alert burden. Instead, workflow factors probably predominate, including timing of alerts in the prescribing process, lack of differentiation of more and less severe alerts, and features of how users interact with alerts. [ABSTRACT FROM AUTHOR]

Introduction: Drug-allergy interaction (DAI) alerts are generated when a known adverse sensitivity-inducing substance is prescribed. A recent study at our institution showed that providers overrode most DAI alerts, including those that warned against potentially life-threatening 'anaphylaxis'.Objective: The aim of this study was to determine the rate of anaphylaxis overrides, the reasons for these overrides, whether the overrides were appropriate, and if harm occurred from overrides.Methods: All DAI alerts, with a reaction of 'anaphylaxis', were analysed for inpatients and outpatients within our health system between January 2009 and December 2011. Only alerts that were triggered by 'definite' alerts (i.e. same ordered medication as documented allergen) were included. Patient charts were reviewed to assess the appropriateness of overrides and potential harm, according to a predetermined set of criteria.Results: A total of 202 inpatient and 16 outpatient alerts met the inclusion criteria. The rate of overrides for 'definite' anaphylaxis DAI alerts was high (inpatient: n = 93, 46.0%; outpatient: n = 11, 68.8%) but appropriate for most overrides in the inpatient (n = 78, 83.9%) and outpatient settings (n = 11, 100%). The most common override reasons in the inpatient and outpatient settings were 'administer per desensitization protocol' (n = 64, 31.7%) and 'patient does not have this allergy' (n = 7, 63.6%), respectively. No harm was associated with overrides in either setting, particularly because many medications were not administered.Conclusions: Overrides of 'definite' anaphylaxis DAI alerts were common and often appropriate. Most overrides were due to desensitizations. Allergy reconciliation for patients could further improve critical decision support. [ABSTRACT FROM AUTHOR]

Introduction: We previously found that patients who initiate clopidogrel while treated with a cytochrome P450 (CYP) 2C19-inhibiting selective serotonin reuptake inhibitor (SSRI) have a higher risk of subsequent ischemic events than patients treated with other SSRIs. It is not known whether initiating an inhibiting SSRI while treated with clopidogrel will also increase risk of ischemic events.Objective: The aim of this study was to assess clinical outcomes following initiation of a CYP2C19-inhibiting SSRI versus initiation of other SSRIs among patients treated with clopidogrel and to update existing evidence on the clinical impact of clopidogrel-SSRI interaction.Methods: Using five US databases (1998-2013), we conducted a cohort study of clopidogrel initiators who encountered treatment with SSRI during their clopidogrel therapy. Patients were matched by propensity score (PS) and followed for as long as they were exposed to both clopidogrel and index SSRI group. Outcomes were a composite ischemic event (myocardial infarction, ischemic stroke, or a revascularization procedure, whichever came first) and a composite major bleeding event (gastrointestinal bleed or hemorrhagic stroke, whichever came first). Results were combined via random-effects meta-analysis with previous evidence from subjects initiating clopidogrel while on SSRI therapy.Results: The PS-matched cohort comprised 2346 clopidogrel users starting CYP2C19-inhibiting SSRI therapy and 16,115 starting other SSRIs (mean age 61 years; 59% female). Compared with those treated with a non-inhibiting SSRI, the hazard ratio (HR) for patients treated with a CYP2C19-inhibiting SSRI was 1.07 (95% confidence interval [CI] 0.82-1.40) for the ischemic outcome and 1.00 (95% CI 0.42-2.36) for bleeding. The pooled estimates were 1.11 (95% CI 1.01-1.22) for ischemic events and 0.80 (95% CI 0.55-1.18) for bleeding.Conclusions: We observed similar estimates of association between the two studies. The updated evidence still indicates a small decrease in clopidogrel effectiveness associated with concomitant exposure to clopidogrel and CYP2C19-inhibiting SSRIs. [ABSTRACT FROM AUTHOR]

As the U.S. healthcare system moves to value-based care, the importance of engaging patients and families continues to intensify. However, simply engaging patients and families to improve their subjective satisfaction will not be enough for providers who want to maximize value. True optimization entails developing deep and long-term relationships with patients. We suggest that healthcare organizations must build such a discipline of "patient relationship management" (PRM) just as companies in non-healthcare industries have done with the concept of customer relationship management (CRM). Some providers have already made strides in this area, but overall it has been underemphasized or ignored by most healthcare systems to date. As healthcare providers work to develop their dedicated PRM systems, tools, and processes, we suggest they may benefit from emulating companies in other industries who have been able to engage their customers in innovative ways while acknowledging the differences between healthcare and other industries. [ABSTRACT FROM AUTHOR]

Background: Mobile applications or 'apps' intended to help people manage their health and chronic conditions are widespread and gaining in popularity. However, little is known about their acceptability and usability for low-income, racially/ethnically diverse populations who experience a disproportionate burden of chronic disease and its complications.Objective: The objective of this study was to investigate the usability of existing mobile health applications ("apps") for diabetes, depression, and caregiving, in order to facilitate development and tailoring of patient-facing apps for diverse populations.Design: Usability testing, a mixed-methods approach that includes interviewing and direct observation of participant technology use, was conducted with participants (n = 9 caregivers; n = 10 patients with depression; and n = 10 patients with diabetes) on a total of 11 of the most popular health apps (four diabetes apps, four depression apps, and three caregiver apps) on both iPad and Android tablets.Participants: The participants were diverse: 15 (58 %) African Americans, seven (27 %) Whites, two (8 %) Asians, two (8 %) Latinos with either diabetes, depression, or who were caregivers.Main Measures: Participants were given condition-specific tasks, such as entering a blood glucose value into a diabetes app. Participant interviews were video recorded and were coded using standard methods to evaluate attempts and completions of tasks. We performed inductive coding of participant comments to identify emergent themes.Key Results: Participants completed 79 of 185 (43 %) tasks across 11 apps without assistance. Three themes emerged from participant comments: lack of confidence with technology, frustration with design features and navigation, and interest in having technology to support their self-management.Conclusions: App developers should employ participatory design strategies in order to have an impact on chronic conditions such as diabetes and depression that disproportionately affect vulnerable populations. While patients express interest in using technologies for self-management, current tools are not consistently usable for diverse patients. [ABSTRACT FROM AUTHOR]

Introduction: The nature of medication errors (MEs) and the frequency of identified or intercepted MEs are not being scrutinized in daily practice in Japan.Objectives: The aim of this study was to clarify the epidemiology of MEs and the risk factors for non-intercepted and unidentified MEs.Methods: The Japan Adverse Drug Events (JADE) study was a prospective cohort study carried out at three tertiary-care teaching hospitals in Japan. Participants were consecutive patients (N = 3459) aged ≥15 years who were admitted to the study wards. MEs were identified by on-site reviews of all medical charts, self-reports, and prescription queries by pharmacists. Two independent physicians reviewed and classified all MEs and adverse drug events and determined the stages at which the MEs occurred and whether there was interception or identification of the MEs.Results: A total of 514 MEs were observed among 433 patients. Sixty-four percent of MEs occurred at the ordering stage. Among these, 60 % were due to duplicate drug orders. Overall, 63 % and 45 % of MEs were not intercepted or identified during hospitalization, respectively. The independent risk factors for non-intercepted MEs were hospitalization in the surgical ward (odds ratio [OR] 2.94) and the intensive care unit (OR 3.57). MEs by resident physicians were more likely to be intercepted (OR 0.52 for non-intercepted MEs).Conclusions: MEs frequently occurred and most at the ordering stage. Almost half of MEs were not intercepted or identified. Many MEs at the later stages were less likely to be intercepted and resulted in actual patient harm. Systems to improve the identification and interception of MEs should be implemented. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article about the cyberattacks against the healthcare systems in the U.S.

Background: Smart infusion pumps have been introduced to prevent medication errors and have been widely adopted nationally in the USA, though they are not always used in Europe or other regions. Despite widespread usage of smart pumps, intravenous medication errors have not been fully eliminated.Objective: Through a systematic review of recent studies and reports regarding smart pump implementation and use, we aimed to identify the impact of smart pumps on error reduction and on the complex process of medication administration, and strategies to maximize the benefits of smart pumps.Methods: The medical literature related to the effects of smart pumps for improving patient safety was searched in PUBMED, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL) (2000-2014) and relevant papers were selected by two researchers.Results: After the literature search, 231 papers were identified and the full texts of 138 articles were assessed for eligibility. Of these, 22 were included after removal of papers that did not meet the inclusion criteria. We assessed both the benefits and negative effects of smart pumps from these studies. One of the benefits of using smart pumps was intercepting errors such as the wrong rate, wrong dose, and pump setting errors. Other benefits include reduction of adverse drug event rates, practice improvements, and cost effectiveness. Meanwhile, the current issues or negative effects related to using smart pumps were lower compliance rates of using smart pumps, the overriding of soft alerts, non-intercepted errors, or the possibility of using the wrong drug library.Conclusion: The literature suggests that smart pumps reduce but do not eliminate programming errors. Although the hard limits of a drug library play a main role in intercepting medication errors, soft limits were still not as effective as hard limits because of high override rates. Compliance in using smart pumps is key towards effectively preventing errors. Opportunities for improvement include upgrading drug libraries, developing standardized drug libraries, decreasing the number of unnecessary warnings, and developing stronger approaches to minimize workarounds. Also, as with other clinical information systems, smart pumps should be implemented with the idea of using continuous quality improvement processes to iteratively improve their use. [ABSTRACT FROM AUTHOR]

BACKGROUND: There is growing interest in developing systems to overcome barriers for acquiring and interpreting family health histories in primary care. OBJECTIVE: To examine the capacity of three different electronic portals to collect family history from patients and deposit valid data in an electronic health record (EHR). DESIGN: Pilot trial. PARTICIPANTS, INTERVENTION: Patients were enrolled from four primary care practices and were asked to collect family health history before a physical exam using either telephone-based interactive voice response (IVR) technology, a secure internet portal, or a waiting room laptop computer, with portal assigned by practice. Intervention practices were compared to a “usual care” practice, where there was no standard workflow to document family history (663 participants in the three intervention arms were compared to 296 participants from the control practice). MAIN MEASURES: New documentation of any family history in a coded EHR field within 30 days of the visit. Secondary outcomes included participation rates and validity. KEY RESULTS: Demographics varied by clinic. Documentation of new family history data was significantly higher, but modest, in each of the three intervention clinics (7.5 % for IVR clinic, 20.3 % for laptop clinic, and 23.1 % for patient portal clinic) versus the control clinic (1.7 %). Patient-entered data on common conditions in first degree relatives was confirmed as valid by a genetic counselor for the majority of cases (ranging from 64 to 82 % in the different arms). CONCLUSIONS: Within primary care practices, valid patient entered family health history data can be obtained electronically at higher rates than a standard of care that depends on provider-entered data. Further research is needed to determine how best to match different portals to individual patient preference, how the tools can best be integrated with provider workflow, and to assess how they impact the use of screening and prevention. [ABSTRACT FROM AUTHOR]

The "Sex assigned at birth" field was complete for 6539 patients (48.7%), and 6445 (48.0%) patients had a completed "Gender identity" field; there were 6166 (45.9%) patients with all three fields completed. In our large health system, narrative text keywords and ICD codes were more sensitive than any gender-related field-based method of identifying transgender patients (Table 1). [Extracted from the article]

Background: While the US FDA maintains a voluntary reporting system, postmarketing adverse drug events (ADEs) are underreported, and this case report-based system does not allow accurate determination of incidence.Objective: The aim of the study was to assess the usefulness of an automated phone pharmacovigilance system for ambulatory patients by comparing systematically collected, patient-reported symptoms to reflect possible ADEs with those reported on the package inserts of two drugs with postmarketing safety concerns, varenicline and zolpidem.Methods: English-speaking adults who received a prescription for zolpidem (n = 370) or varenicline (n = 107) from a primary care physician at one of 11 participating clinics, and who participated in the pharmacovigilance system during 2008-2010, were included in the study. Patients were called approximately 4 weeks following their visit and asked to complete a standard script that asked about adherence and pre-specified symptoms.Main Outcome Measures: The main outcome measures were elicited rates of pre-specified symptoms or possible ADEs.Results: Compared with the package insert, patients taking zolpidem were significantly (p < 0.001) more likely to report fatigue (9.0% vs 1.0%), itching (4.5% vs 1.0%) and muscle aches (5.6% vs 1.0%). Elicited rates of depression and hallucination were similar to those reported in the package insert. Patients taking varenicline were significantly more likely to report confusion (1.7% vs 0.1%), depression (3.4% vs 0.1%), fatigue (6.0% vs 1.0%), hallucinations (1.7% vs 0.1%), muscle aches (6.0% vs 1.0%) and sexual dysfunction (4.3% vs 0.1%).Conclusions: Automated phone pharmacovigilance can provide estimates of possible ADEs in clinical practice. In the case of varenicline, these data support some of the safety concerns that have come to light postmarketing, while others such as depression and hallucination related to zolpidem were not detected. These data highlight the potential value of, and innovative ways of collecting, information about possible ADEs directly from patients. [ABSTRACT FROM AUTHOR]

BACKGROUND Provider and patient reminders can be effective in increasing rates of preventive screenings and vaccinations. However, the effect of patient-directed electronic reminders is understudied. OBJECTIVE To determine whether providing reminders directly to patients via an electronic Personal Health Record (PHR) improved adherence to care recommendations. DESIGN We conducted a cluster randomized trial without blinding from 2005 to 2007 at 11 primary care practices in the Partners HealthCare system. PARTICIPANTS A total of 21,533 patients with access to a PHR were invited to the study, and 3,979 (18.5%) consented to enroll. INTERVENTIONS Patients in the intervention arm received health maintenance (HM) reminders via a secure PHR "eJournal," which allowed them to review and update HM and family history information. Patients in the active control arm received access to an eJournal that allowed them to input and review information related to medications, allergies and diabetes management. MAIN MEASURES The primary outcome measure was adherence to guideline-based care recommendations. KEY RESULTS Intention-to-treat analysis showed that patients in the intervention arm were significantly more likely to receive mammography (48.6% vs 29.5%, p = 0.006) and influenza vaccinations (22.0% vs 14.0%, p = 0.018). No significant improvement was observed in rates of other screenings. Although Pap smear completion rates were higher in the intervention arm (41.0% vs 10.4%, p < 0.001), this finding was no longer significant after excluding women's health clinics. Additional on-treatment analysis showed significant increases in mammography (p = 0.019) and influenza vaccination (p = 0.015) for intervention arm patients who opened an eJournal compared to control arm patients, but no differences for any measure among patients who did not open an eJournal. CONCLUSIONS Providing patients with HM reminders via a PHR may be effective in improving some elements of preventive care. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) causes substantial morbidity and mortality; however, there are limited data to comprehensively assess quality of care in this area.Objective: To assess quality of care for CKD according to patient risk and identify correlates of improved care delivery.Design: Retrospective cohort.Setting: Fifteen health centers within a multi-site group practice in eastern Massachusetts.Participants: 166 primary care physicians caring for 11,774 patients with stages 3 or 4 CKD defined as two estimated glomerular filtration rates (eGFR) between 15 and 60.Main Measures: Two measures of kidney disease monitoring, five measures of cardiovascular disease management, four measures of metabolic bone disease and anemia management, and one measure of drug safety were extracted from the electronic health record. Primary care recognition of CKD was assessed as a problem list diagnosis, and nephrology co-management was assessed as at least one visit with a nephrologist in the prior 12 months.Key Results: Overall, 46% of patients were high risk for death based on the presence of diabetes, proteinuria, or an eGFR <45. Seventy percent of patients lacked annual urine protein testing, 46% had a blood pressure ≥130/80 mmHg and 25% were not receiving appropriate angiotensin blockade. Appropriate screening for anemia was common (76%), while screening rates for metabolic bone disease were low. Use of potentially harmful drugs was common (26%). Primary care physician recognition and nephrology co-management were both associated with improved quality of care, though rates of both were low (24% and 10%, respectively).Conclusions: Significant deficiencies in the quality of CKD care exist. Opportunities for improvement include increasing physician recognition of CKD and improving collaborative care with nephrology. [ABSTRACT FROM AUTHOR]

Background: The epidemiology of adverse drug events (ADEs) and medication errors has received little evaluation outside the U.S. and Europe, and extrapolating from these data might not be valid, especially regarding selecting and prioritizing solutions.Objective: To assess the incidence and preventability of ADEs and medication errors in Japan.Design: The Japan Adverse Drug Events (JADE) study was a prospective cohort study.Patients: A cohort of 3,459 adults admitted to a stratified random sample of seven medical and eight surgical wards and three intensive care units in three tertiary care hospitals over 6 months.Main Measures: We measured ADE and medication error rates from daily reviews of charts, laboratories, incident reports, and prescription queries by on-site reviewers; presence of a signal was considered an incident. Two independent physicians reviewed incidents to determine whether they were ADEs or medication errors and to assess severity and preventability.Key Results: We identified 1,010 ADEs and 514 medication errors (incidence: 17.0 and 8.7 per 1,000 patient-days, respectively) during the study period. Among ADEs, 1.6%, 4.9% and 33% were fatal, life-threatening and serious, respectively. Among ADEs, 14% were preventable. The rate per admission was 29 per 100 admissions, higher than in U.S. studies because associated with of the long length of hospital stay in Japan (mean, 17 days).Conclusions: The epidemiology and nature of ADEs and medication errors in Japan were similar to other countries, although more frequent per admission. Solutions that worked in these countries might thus improve medication safety in Japan, as could shortening hospital length of stay. [ABSTRACT FROM AUTHOR]

Context: Medications represent a major cause of harm and are costly for hospitalized patients, but more is known about these issues in large academic hospitals than in smaller hospitals.Objective: To assess the incidence of adverse drug events (ADEs) in six community hospitals.Design: Multicenter, retrospective cohort study.Setting: Six Massachusetts community hospitals with 100 to 300 beds.Patients: From 109,641 adult patients hospitalized from January 2005 through August 2006, a random sample of 1,200 patients was drawn, 200 per site.Main Outcome Measures: ADEs and preventable ADEs.Methods: Presence of an ADE was evaluated using an adaptation of a trigger instrument developed by the Institute for Health Care Improvement. Independent reviewers classified events by preventability, severity, and potential for preventability by computerized physician order entry (CPOE).Results: A total of 180 ADEs occurred in 141 patients (rate, 15.0/100 admissions). Overall, 75% were preventable. ADEs were rated as serious in 49.4% and life threatening in 11.7%. Patients with ADEs were older (mean age, 74.6 years, p < 0.001), more often female (60.3%, p = 0.61), and more often Caucasian (96.5%, p < 0.001) than patients without ADEs. Of the preventable ADEs, 81.5% were judged potentially preventable by CPOE.Conclusions: The incidence of ADEs in these community hospital admissions was high, and most ADEs were preventable, mostly through CPOE. These data suggest that CPOE may be beneficial in this setting. [ABSTRACT FROM AUTHOR]

Background: Selective cyclo-oxygenase 2 inhibitors ('coxibs') have been demonstrated to increase cardiovascular risk, but the cumulative burden of adverse effects in the US population is uncertain.Objective: To quantify cardiovascular and gastrointestinal (GI) haemorrhage disease burden from coxibs and traditional 'non-selective' non-steroidal anti-inflammatory drugs (t-NSAIDs) in the US population.Design, Setting and Participants: Adult respondents from the 1999-2003 Medical Expenditure Panel Survey, a representative sample of the US population which first became available in December 2006, were included. Respondents were followed for 2 years. Exposure was defined by two or more prescriptions of rofecoxib, celecoxib or a t-NSAID in the first year.Main Outcome Measures: Acute myocardial infarction (AMI), stroke and/or GI haemorrhage in the year following exposure.Results: Exposure to rofecoxib was associated with an adjusted odds ratio (OR) of 3.30 for AMI (95% CI 1.41, 7.68; p=0.01) and 4.28 for GI haemorrhage (95% CI 1.33, 13.71; p=0.02). Celecoxib was not associated with a statistically significant effect on AMI (OR 1.44; 95% CI 0.57, 3.69; p=0.44), but there was an OR of 2.43 for stroke (95% CI 1.05, 5.58; p=0.04) and 4.98 for GI haemorrhage (95% CI 2.22, 11.17; p<0.001). The group of t-NSAIDs was not associated with a significant adverse effect on AMI (OR 1.47; 95% CI 0.76, 2.84; p=0.25) or stroke (OR 1.26; 95% CI 0.42, 3.81; p=0.68), and was associated with an OR of 2.38 for GI haemorrhage (CI 1.04, 5.46; p=0.04). In the 1999-2004 period rofecoxib was associated with 46 783 AMIs and 31 188 GI haemorrhages; celecoxib with 21 832 strokes and 69 654 GI haemorrhages; resulting in an estimated 26 603 deaths from both coxibs. The t-NSAID group was associated with an excess of 87 327 GI haemorrhages and 9606 deaths in the same period.Conclusions: Iatrogenic effects of coxibs in the US population were substantial, posing an important public health risk. Drugs that were rapidly accepted for assumed safety advantages proved instead to have caused substantial injury and death. [ABSTRACT FROM AUTHOR]

Background: Somatoform disorders are an important factor in functional disability and role impairment, though their independent contribution to disability has been unclear because of prevalent medical and psychiatric comorbidity.Objectives: To assess the extent of the overlap of somatization with other psychiatric disorders and medical problems, to compare the functional disability and role impairment of somatizing and non-somatizing patients, and to determine the independent contribution of somatization to functional disability and role impairment.Design: Patients were surveyed with self-report questionnaires assessing somatization, psychiatric disorder, and role impairment. Medical morbidity was indexed with a computerized medical record audit.Participants: Consecutive adults making scheduled visits to their primary care physicians at two hospital-affiliated primary care practices on randomly chosen days.Measurements: Intermediate activities of daily living, social activities, and occupational disability.Results: Patients with somatization, as well as those with serious medical and psychiatric illnesses, had significantly more impairment of activities of daily life and social activities. When these predictors were considered simultaneously in a multivariable regression, the association with somatization remained highly significant and was comparable to or greater than many major medical conditions.Conclusions: Patients with somatization had substantially greater functional disability and role impairment than non-somatizing patients. The degree of disability was equal to or greater than that associated with many major, chronic medical disorders. Adjusting the results for psychiatric and medical co-morbidity had little effect on these findings. [ABSTRACT FROM AUTHOR]

Background: We conducted a cluster randomized controlled trial to examine the effectiveness of computerized decision support (CDS) designed to improve hypertension care and outcomes in a racially diverse sample of primary care patients.Methods: We randomized 2,027 adult patients receiving hypertension care in 14 primary care practices to either 18 months of their physicians receiving CDS for each hypertensive patient or to usual care without computerized support for the control group. We assessed prescribing of guideline-recommended drug therapy and levels of blood pressure control for patients in each group and examined if the effects of the intervention differed by patients' race/ethnicity using interaction terms.Measurements and Main Results: Rates of blood pressure control were 42% at baseline and 46% at the outcome visit with no significant differences between groups. After adjustment for patients' demographic and clinical characteristics, number of prior visits, and levels of baseline blood pressure control, there were no differences between intervention groups in the odds of outcome blood pressure control. The use of CDS to providers significantly improved Joint National Committee (JNC) guideline adherent medication prescribing compared to usual care (7% versus 5%, P < 0.001); the effects of the intervention remained after multivariable adjustment (odds ratio [OR] 1.39 [CI, 1.13-1.72]) and the effects of the intervention did not differ by patients' race and ethnicity.Conclusions: CDS improved appropriate medication prescribing with no improvement in disparities in care and overall blood pressure control. Future work focusing on improvement of these interventions and the study of other practical interventions to reduce disparities in hypertension-related outcomes is needed. [ABSTRACT FROM AUTHOR]

Objective: Computerised monitors can detect and, with clinical intervention, often prevent or ameliorate adverse drug events (ADEs). We evaluated whether a computer-based alerting system was useful in a community hospital setting.Methods: We evaluated 6 months of retrospectively collected medication and laboratory data from a 140-bed community hospital, and applied the rules from a computerised knowledge base to determine if the resulting alerts might have allowed a clinician to prevent or lessen harm related to medication toxicity. We randomly selected 11% (n = 58, of which 56 were available) of charts deemed to be high- or critical-priority alerts, based on the likelihood of the alerts being associated with injury, to determine the frequencies of ADEs and preventable ADEs.Results: In 6 months, there were 8829 activations of the rule set, generating a total of 3547 alerts. Of these, 528 were of high or critical priority, 664 were of medium priority and 2355 were of low priority. Chart review among the sample (56 charts) of high- or critical-priority alerts found five non-preventable and two preventable ADEs, suggesting that among the total high- or critical-priority alerts alone, there would be approximately 94 non-preventable ADEs and 37 preventable ADEs annually in this hospital that could be detected using this method.Conclusions: Computer-based rules engines have the potential to identify and, with clinical intervention, mitigate preventable ADEs, and implementation is feasible in community hospitals without an advanced information technology application. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To evaluate the psychological, socio-behavioral, and medical implications of apparently false-positive prostate cancer screening results. METHODS: One hundred and twenty-one men with a benign prostate biopsy performed in response to a suspicious screening test (biopsy group) and 164 men with a normal prostate-specific antigen (PSA) test result (normal PSA group) responded to a questionnaire 6 weeks, 6 and 12 months after their biopsy or PSA test. RESULTS: The mean (± SD) age of respondents was 61 ± 9 years (range, 41 to 88 years). One year later. 26% (32/121) of men in the biopsy group reported having worried ‘a lot’ or ‘some of the time’ that they may develop prostate cancer, compared with 6% (10/164) in the normal PSA group (P<.001). Forty-six percent of the biopsy group reported thinking their wife or significant other was concerned about prostate cancer, versus 14% in the normal PSA group (P<.001). Medical record review showed that biopsied men were more likely than those in the normal PSA group to have had at least 1 follow-up PSA test over the year (73% vs 42%, P<.00 1). more likely to have had another biopsy (15% vs 1%, P< .001), and more likely to have visited a urologist (71% vs 13%, P<.001). CONCLUSION: One year later, men who underwent prostate biopsy more often reported worrying about prostate cancer. In addition, there were related psychological, socio-behavioral, and medical care implications. These hidden tolls associated with screening should be considered in the discussion about the benefits and risks of prostate cancer screening. [ABSTRACT FROM AUTHOR]

Purpose: Little is known about how well hospitalized patients can identify errors or injuries in their care. Accordingly, the purpose of this study was to elicit incident reports from hospital inpatients in order to identify and characterize adverse events and near-miss errors.Subjects: We conducted a prospective cohort study of 228 adult inpatients on a medicine unit of a Boston teaching hospital.Methods: Investigators reviewed medical records and interviewed patients during the hospitalization and by telephone 10 days after discharge about "problems,""mistakes," and "injuries" that occurred. Physician investigators classified patients' reports. We calculated event rates and used multivariable Poisson regression models to examine the factors associated with patient-reported events.Results: Of 264 eligible patients, 228 (86%) agreed to participate and completed 528 interviews. Seventeen patients (8%) experienced 20 adverse events; 1 was serious. Eight patients (4%) experienced 13 near misses; 5 were serious or life threatening. Eleven (55%) of 20 adverse events and 4 (31%) of 13 near misses were documented in the medical record, but none were found in the hospital incident reporting system. Patients with 3 or more drug allergies were more likely to report errors compared with patients without drug allergies (incidence rate ratio 4.7, 95% CI 1.7, 13.4).Conclusion: Inpatients can identify adverse events affecting their care. Many patient-identified events are not captured by the hospital incident reporting system or recorded in the medical record. Engaging hospitalized patients as partners in identifying medical errors and injuries is a potentially promising approach for enhancing patient safety. [ABSTRACT FROM AUTHOR]