Impact of suboptimal or inappropriate treatment on healthcare resource use and cost among patients with uncomplicated urinary tract infection: an analysis of integrated delivery network electronic health records.

BACKGROUND: Although uncomplicated urinary tract infections (uUTIs; occurring in female patients without urological abnormalities or history of urological procedures or complicating comorbidities) are one of the most common community infections in the United States (US), limited data are available concerning associations between antibiotic resistance, suboptimal prescribing, and the economic burden of uUTI. We examined the prevalence of suboptimal antibiotic prescribing and antibiotic resistance and its effects on healthcare resource use and costs. METHODS: This retrospective cohort study utilized electronic health record data from a large Mid-Atlantic US integrated delivery network database, collected July 2016-March 2020. Female patients aged ≥ 12 years with a uUTI, who received ≥ 1 oral antibiotic treatment within ± 5 days of index uUTI diagnosis, and had ≥ 1 urine culture with antimicrobial susceptibility test, were eligible for inclusion in the study. The study examined the proportion of antibiotics that were inappropriately or suboptimally prescribed among patients with confirmed uUTI, and total healthcare costs (all-cause and UTI-related) within 6 months after a uUTI, stratified by antibiotic susceptibility and/or inappropriate or suboptimal treatment. Patient outcomes were assessed after 1:1 propensity score matching of patients with antibiotic-susceptible versus not-susceptible isolates and then by other covariates (e.g., demographics and recent healthcare use). A similar propensity score calculation was used to analyze the effect of inappropriate/suboptimal treatment on health outcomes. Costs were adjusted to 2020 US dollars ($). RESULTS: Among 2565 patients with a uUTI included in the analysis, the most commonly prescribed antibiotics were nitrofurantoin (61%), trimethoprim-sulfamethoxazole (19%), and ciprofloxacin (15%). More than one-third of the sample (40.2%) had isolates that were not-susceptible to ≥ 1 antibiotic indicated for treating patients with uUTI. Two-thirds (66.6%) of study-eligible patients were prescribed appropriate treatment; 29.9% and 11.9% were prescribed suboptimal and/or inappropriate treatment, respectively. Inappropriate or suboptimally prescribed patients had greater all-cause and UTI-related costs compared with appropriately prescribed patients. Differences were most striking among patients with antibiotic not-susceptible isolates. CONCLUSIONS: These findings highlight how the increasing prevalence of antibiotic resistance combined with suboptimal treatment of patients with uUTI increases the burden on healthcare systems. The finding underlines the need for improved prescribing accuracy by better understanding regional resistance rates and developing improved diagnostic tests.

Novel Techniques to Assess Predictive Systems and Reduce Their Alarm Burden.

Machine prediction algorithms (e.g., binary classifiers) often are adopted on the basis of claimed performance using classic metrics such as precision and recall. However, classifier performance depends heavily upon the context (workflow) in which the classifier operates. Classic metrics do not reflect the realized performance of a predictor unless certain implicit assumptions are met, and these assumptions cannot be met in many common clinical scenarios. This often results in suboptimal implementations and in disappointment when expected outcomes are not achieved. One common failure mode for classic metrics arises when multiple predictions can be made for the same event, particularly when redundant true positive predictions produce little additional value. This describes many clinical alerting systems. We explain why classic metrics cannot correctly represent predictor performance in such contexts, and introduce an improved performance assessment technique using utility functions to score predictions based on their utility in a specific workflow context. The resulting utility metrics (u-metrics) explicitly account for the effects of temporal relationships and other sources of variability in prediction utility. Compared to traditional measures, u-metrics more accurately reflect the real-world costs and benefits of a predictor operating in a realized context. The improvement can be significant. We also describe a formal approach to snoozing, a mitigation strategy in which some predictions are suppressed to improve predictor performance by reducing false positives while retaining event capture. Snoozing is especially useful for predictors that generate interruptive alarms. U-metrics correctly measure and predict the performance benefits of snoozing, whereas traditional metrics do not.

Economic burden of antibiotic-not-susceptible isolates in uncomplicated urinary tract infection: Analysis of a US integrated delivery network database.

BACKGROUND: Uncomplicated urinary tract infections (uUTIs) are one of the most common bacterial infections in the United States (US). Contemporary data are important for understanding the health economic impact of antimicrobial-resistant uUTIs. We compared the economic burden among patients with uUTI isolates susceptible or not-susceptible to the initial antibiotic prescription. METHODS: This retrospective cohort study utilized electronic health record data (1 July 2016-31 March 2020) from a large Mid-Atlantic US integrated delivery network database. Patients were females aged ≥ 12 years with a uUTI, who received oral antibiotic treatment and had ≥ 1 urine culture within ± 5 days of diagnosis. The primary outcome was the difference in healthcare resource use and costs (all-cause, urinary tract infection [UTI]-related) among patients with susceptible versus not-susceptible isolates during the 6 months after the index uUTI diagnosis. Secondary outcomes included: pharmacy costs, hospital admissions and emergency department visits, as well as the probability of uUTI progressing to complicated UTI (cUTI) between patients with susceptible and not-susceptible isolates. Patient outcomes were compared using 1:1 propensity score matching. Winsorized costs were adjusted to 2020 quarter 1 US dollars ($). RESULTS: A total of 2565 patients were eligible for analysis. The propensity score-matched sample comprised 2018 patients, with an average age of 44.0 and 41.0 years for the susceptible and not-susceptible populations, respectively. In the 6 months post-index uUTI event, patients with not-susceptible isolates had significantly more all-cause prescriptions orders (+ 1.41 [P = 0.001]), UTI-related prescriptions orders (+ 0.26 [P < 0.001]) and a higher probability of all-cause inpatient (+ 1.4% [P = 0.009]), outpatient (+ 6.1% [P = 0.006]), or UTI-related outpatient (+ 3.7% [P = 0.039]) encounters. Patients with a uUTI and an antibiotic-not-susceptible isolate were significantly more likely to progress to cUTI than those with susceptible isolates (odds ratio: 2.35 [confidence interval: 1.66-3.33; P < 0.001]). Over 6 months, patients with not-susceptible versus susceptible isolates had significantly higher all-cause costs (+ $426 [P = 0.031]) and UTI-related costs (+ $157 [P = 0.034]). CONCLUSIONS: Patients with a uUTI caused by antibiotic-not-susceptible isolates had higher healthcare resource usage, costs, and increased likelihood of progressing to cUTI than those with antibiotic-susceptible isolates.

Multi-center implementation of automated age-adjusted D-dimer results reduces unnecessary PE imaging.

BACKGROUND: Several previous studies have investigated the clinical utility of age-adjusted D-dimer cutoffs for diagnosing pulmonary embolism (PE). OBJECTIVES: We performed a pre/post implementation study, using data from a mid-Atlantic healthcare system comprising 6 hospitals and 400,000 ED visits to determine whether implementing age adjusted D-dimer cutoffs reduced the number of imaging tests performed. METHODS: Retrospective study of all patients who had a D-dimer performed during ED visits between September 2015 to September 2018. On March 21, 2017, the D-dimer upper limit of normal system-wide was increased for patients over 50 to: Age (years) x 0.01µg/mL. D-dimer results were displayed as normal or high based on automated age adjustment. EHR Chart review was performed 1.5 years prior to implementation of age-adjusted D-dimer cutoffs, as well as 1.5 years after to evaluate mortality and test accuracy characteristics such as false negative rates. Comparisons were made using chi-square testing. RESULTS: 22,302 D-dimers were performed pre-implementation of which 10,837 (48.6%) were positive resulting in 7218 (32.3%) imaging studies. After implementation of age-adjusted d-dimer, 25,082 were performed of which 10,851 (43.2%) were positive resulting in 7017 (28.0%) imaging studies. (pre: 48.6%, post: 43.2%; p < 0.01). A significantly lower proportion of patients had a positive d-dimer (pre: 48.6%, post: 43.2%; p < 0.01) and underwent imaging post-implementation (pre: 32.3%, post: 28.0%; p < 0.05) a relative risk reduction of 13.3. This absolute risk reduction of 4.4% is associated with 1104 less scans in the post-implementation group while still increasing test accuracy from 53.7% to 59.2% (p < 0.05). CONCLUSION: Implementation of an automated age-adjusted D-dimer positive reference value reduced CT and V/Q imaging in this population by 4.4% while increasing test accuracy in a regional, heterogeneous six-hospital system.

Child and adolescent benzodiazepine exposure and overdose in the United States: 16 years of poison center data.

Background: Recently, there has been an increase in prescription drug abuse and related fatalities. Although opioid analgesics are commonly implicated, there have been significant increases in the prevalence of benzodiazepine exposures and overdoses.Objective: To describe national trends in pediatric benzodiazepine exposures from 2000 to 2015.Methods: A retrospective database analysis was conducted. Data regarding benzodiazepine exposures in children ages 0 to <18 years reported to participating United States poison centers from January 2000 through December 2015 were obtained from the National Poison Data System. Population data were obtained from the US Census Bureau to determine annual population estimates. Data were analyzed using chi-square tests.Results: A total of 296,838 pediatric benzodiazepine exposures were identified during the study period. The rate of pediatric benzodiazepine exposure increased 54% between 2000 and 2015. The severity of medical outcomes also increased, as did the prevalence of co-ingestion of multiple drugs, especially in children ages 12 to <18 years. Nearly half of all reported exposures in 2015 were documented as intentional abuse, misuse, or attempted suicide, reflecting a change from prior years. The most commonly identified pediatric benzodiazepines of exposures were alprazolam, clonazepam, and lorazepam.Conclusions: The rate and severity of reported pediatric benzodiazepine exposure is increasing over time. Adolescent exposures are of specific concern, as co-ingestion and intentional abuse were found to be more common in this group. Medical providers and caretakers should be cognizant of this growing epidemic to avoid preventable harm to adolescents, young children, and infants.

Data-driven decisions for reducing readmissions for heart failure: general methodology and case study.

BACKGROUND: Several studies have focused on stratifying patients according to their level of readmission risk, fueled in part by incentive programs in the U.S. that link readmission rates to the annual payment update by Medicare. Patient-specific predictions about readmission have not seen widespread use because of their limited accuracy and questions about the efficacy of using measures of risk to guide clinical decisions. We construct a predictive model for readmissions for congestive heart failure (CHF) and study how its predictions can be used to perform patient-specific interventions. We assess the cost-effectiveness of a methodology that combines prediction and decision making to allocate interventions. The results highlight the importance of combining predictions with decision analysis. METHODS: We construct a statistical classifier from a retrospective database of 793 hospital visits for heart failure that predicts the likelihood that patients will be rehospitalized within 30 days of discharge. We introduce a decision analysis that uses the predictions to guide decisions about post-discharge interventions. We perform a cost-effectiveness analysis of 379 additional hospital visits that were not included in either the formulation of the classifiers or the decision analysis. We report the performance of the methodology and show the overall expected value of employing a real-time decision system. FINDINGS: For the cohort studied, readmissions are associated with a mean cost of $13,679 with a standard error of $1,214. Given a post-discharge plan that costs $1,300 and that reduces 30-day rehospitalizations by 35%, use of the proposed methods would provide an 18.2% reduction in rehospitalizations and save 3.8% of costs. CONCLUSIONS: Classifiers learned automatically from patient data can be joined with decision analysis to guide the allocation of post-discharge support to CHF patients. Such analyses are especially valuable in the common situation where it is not economically feasible to provide programs to all patients.

Case study: the health SmartLibrary experiences in web personalization and customization at the Galter health sciences library, Northwestern University.

QUESTION: How can the user's access to health information, especially full-text articles, be improved? The solution is building and evaluating the Health SmartLibrary (HSL). SETTING: The setting is the Galter Health Sciences Library, Feinberg School of Medicine, Northwestern University. METHOD: The HSL was built on web-based personalization and customization tools: My E-Resources, Stay Current, Quick Search, and File Cabinet. Personalization and customization data were tracked to show user activity with these value-added, online services. MAIN RESULTS: Registration data indicated that users were receptive to personalized resource selection and that the automated application of specialty-based, personalized HSLs was more frequently adopted than manual customization by users. Those who did customize customized My E-Resources and Stay Current more often than Quick Search and File Cabinet. Most of those who customized did so only once. CONCLUSION: Users did not always take advantage of the services designed to aid their library research experiences. When personalization is available at registration, users readily accepted it. Customization tools were used less frequently; however, more research is needed to determine why this was the case.

Indistinguishable from magic: health and wellness in a future of sufficiently advanced technology.

We describe a future in which health and wellness are transformed by (1) the availability of definitive and unambiguous tests to prove or disprove each diagnosis, (2) new methods based in systems biology to help unravel the web of messages transmitted across cellular and subcellular networks, and (3) universal access to data that has been freed from data silos to produce true data liquidity for a constellation of purposes ranging from personal health management to population health research. We believe the resulting "connected health" environment will have a profound impact on every aspect of modern life.

Searching electronic health records for temporal patterns in patient histories: a case study with microsoft amalga.

As electronic health records (EHR) become more widespread, they enable clinicians and researchers to pose complex queries that can benefit immediate patient care and deepen understanding of medical treatment and outcomes. However, current query tools make complex temporal queries difficult to pose, and physicians have to rely on computer professionals to specify the queries for them. This paper describes our efforts to develop a novel query tool implemented in a large operational system at the Washington Hospital Center (Microsoft Amalga, formerly known as Azyxxi). We describe our design of the interface to specify temporal patterns and the visual presentation of results, and report on a pilot user study looking for adverse reactions following radiology studies using contrast.

A gesture-based tool for sterile browsing of radiology images.

The use of doctor-computer interaction devices in the operation room (OR) requires new modalities that support medical imaging manipulation while allowing doctors' hands to remain sterile, supporting their focus of attention, and providing fast response times. This paper presents "Gestix," a vision-based hand gesture capture and recognition system that interprets in real-time the user's gestures for navigation and manipulation of images in an electronic medical record (EMR) database. Navigation and other gestures are translated to commands based on their temporal trajectories, through video capture. "Gestix" was tested during a brain biopsy procedure. In the in vivo experiment, this interface prevented the surgeon's focus shift and change of location while achieving a rapid intuitive reaction and easy interaction. Data from two usability tests provide insights and implications regarding human-computer interaction based on nonverbal conversational modalities.

Metrics in the science of surge.

Metrics are the driver to positive change toward better patient care. However, the research into the metrics of the science of surge is incomplete, research funding is inadequate, and we lack a criterion standard metric for identifying and quantifying surge capacity. Therefore, a consensus working group was formed through a "viral invitation" process. With a combination of online discussion through a group e-mail list and in-person discussion at a breakout session of the Academic Emergency Medicine 2006 Consensus Conference, "The Science of Surge," seven consensus statements were generated. These statements emphasize the importance of funded research in the area of surge capacity metrics; the utility of an emergency medicine research registry; the need to make the data available to clinicians, administrators, public health officials, and internal and external systems; the importance of real-time data, data standards, and electronic transmission; seamless integration of data capture into the care process; the value of having data available from a single point of access through which data mining, forecasting, and modeling can be performed; and the basic necessity of a criterion standard metric for quantifying surge capacity. Further consensus work is needed to select a criterion standard metric for quantifying surge capacity. These consensus statements cover the future research needs, the infrastructure needs, and the data that are needed for a state-of-the-art approach to surge and surge capacity.

Wireless handheld computers and voluntary utilization of computerized prescribing systems in the emergency department.

Illegible or invalid hand-written prescriptions can result in avoidable medical errors. Computer-based prescribing can mitigate the problem. An observational study was performed to examine the effect of wireless handheld computers (handhelds) on voluntary utilization of computerized prescribing within an Emergency Department. Handhelds with prescription-writing software were provided to physicians and the numbers of hand-written and computer-generated prescriptions were compared before and after the introduction of the handhelds. The resulting increase in computer-based prescribing was statistically significant and was observed largely among physicians who already used desktop computers for prescribing. The study concluded that handhelds increased voluntary utilization of computerized prescribing, but that the physicians most likely to use handhelds were those who already used desktop-based prescribing.

A real-time gesture interface for hands-free control of electronic medical records.

Whether attempting to review digital radiologic images during a procedure or reviewing labs on a clinical ward, computer keyboards and mice are potential sources for contamination of clinicians during sterile and non-sterile activities related to clinical care. The authors describe and demonstrate a live system prototype for hands-free, gesture-based control of an electronic medical record (EMR) system.

Automated patient facial image capture to reduce medical error.

The authors describe their experiences creating technology to automatically capture facial images from patients during triage and registration for integration into the electronic medical record (EMR) to reduce data retrieval and data entry errors. The prototype system was tested across a variety of ethnicities with facial images captured successfully in 100% of cases with a median time to capture of 0.75 seconds.

Improving a UMLS based allergy list for use in live electronic medical record systems.

The SNOMED allergy subset available through the UMLS has a variety of deficits that are substantial barriers to use in live clinical practice. These authors describe a method of enhancing a UMLS based allergy list by combining concepts from other terminologies found within the UMLS. This method resulted in a three-fold increase in the coverage allergy list compared to the standard SNOMED allergy subset.

Use of medical robotics in biothreat situations.

SARS, Avian Flu and other infectious and potentially highly transmissible diseases are threats to the entire healthcare workforce. Complete bio-isolation or the use of biohazard suits are not practical solutions for routine day-to-day patient-doctor interactions with highly infectious patients. The authors share their initial research experiences with utilizing medical robots for teleconferencing and other clinical activities to overcome these hurdles.

Automated medical image library creation for education.

The authors describe a method to create a medical teaching library that is automatically maintained, contains tens of thousands of radiologic images and is built using existing, internal, hospital dictations, radiologic images, and an off-the-shelf commercial search engine product (Google Inc.).

Computerized physician order entry and online decision support.

Computerized physician order entry (CPOE) and decision support systems (DSS) can reduce certain types of error but often slow clinicians and may increase other types of error. The net effect of these systems on an emergency department (ED) is unknown. The consensus participants combined published evidence with expert opinion to outline recommendations for success. These include seamless integration of CPOE and DSS into systems and workflow; ensuring access to Internet-based and other online support material in the clinical arena; designing systems specifically for the ED and measuring their impact to ensure an overall benefit; ensuring that CPOE systems provide error and interaction checking and facilitate weight- and physiology-based dosing; using interruptive alerts only for the highest-severity events; providing a simple, vendor-independent interface for institutional customization of CPOE alert thresholds; maximizing the use of automated systems and passive data capture; and ensuring the widespread availability of CPOE and DSS using secure wireless and portable technologies where appropriate. Decisions regarding CPOE and DSS in the ED should be guided by the ED chair or designee. Much of what is believed to be true regarding CPOE and DSS has not been adequately studied. Additional CPOE and DSS research is needed quickly, and this research should receive funding priority. DSS and CPOE hold great promise to improve patient care, but not all systems are equal. Evidence must guide these efforts, and the measured outcomes must consider the many factors of quality care.