Utilization Metrics Research Articles

Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore

ObjectiveWe aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention....

Evidence-based psychotherapy trends among posttraumatic stress disorder patients in a national healthcare system, 2001-2014.

Although evidence-based psychotherapies (EBPs) for posttraumatic stress disorder (PTSD) were implemented starting in 2005 in the veterans health administration (VHA), the largest national healthcare system in the U.S., the rate of initiation (uptake) and prevalence of these treatments in each calendar year have not been determined. We aimed to elucidate two metrics of EBP utilization, uptake and prevalence, following implementation. Cohort study of Iraq and Afghanistan veterans in VHA (N = 181,620) with a PTSD diagnosis and ≥1 psychotherapy-coded outpatient visit from 2001 to 2014. Using natural language processing techniques, annual and cumulative uptake and prevalence rates from 2001 to 2014were calculated for each of the two EBPs for PTSD, cognitive processing therapy (CPT) and prolonged exposure (PE) therapy. Annual uptake of CPT increased during most years, reaching a maximum of 11.1%. Annual uptake of PE showed little change until 2008 and then increased, reaching a maximum of 4.4%. The annual prevalence of CPT increased throughout the study, reaching a maximum of 14.6%. The annual prevalence of PE increased to a maximum of 5.0% in 2010, but then flattened and declined. Annual uptake of minimally adequate CPT increased a to maximum of 5% in 2014. Annual uptake of minimally adequate PE increased to a maximum of 1.2% in 2010.The cumulative prevalence of CPT was 19.9% and cumulative prevalence for PE was 7.5%. Access to EBPs for PTSD modestly increased for Iraq and Afghanistan veterans after nationwide implementation efforts. Further expanding the reach to veterans is critical, given low rates of minimally adequate EBPs for PTSD.

Resource allocation strategies for protein purification operations

We analyze resource allocation challenges in protein purification operations where differences in scientist capabilities can lead to significantly different outcomes. We use queuing models to capture the underlying dynamics and quantify the performance of different strategies based on solutions obtained using the matrix-geometric approach. We show that certain partial flexibility structures coupled with appropriate priority rules can yield very efficient system performance. We also define a new server utilization metric that can be very effective in rank ordering strategies. Through numerical studies, we provide useful rules for the biomanufacturers to achieve higher profits and shorter lead times.

A Delay Optimal Multiclass Packet Scheduler for General M2M Uplink

The uplink data arriving at the Machine-to-Machine (M2M) Application Server (AS) via M2M Aggregators (MAs) is fairly heterogeneous along several dimensions such as maximum permissible packet delay, arrival rate, and payload size, thus necessitating the design of Quality-of-Service (QoS) aware packet schedulers. In this paper, we classify the M2M uplink data into multiple QoS classes and use sigmoidal function to map the delay requirements of each class onto utility functions. We propose a delay-optimal multiclass packet scheduler at AS obtained by iteratively searching for the optimal fraction of time-sharing between all preemptive priority scheduling policies, such that it maximizes a proportionally fair system utility metric. The iterative optimization process leads to reduced complexity, thus facilitating its online implementation. We then extend this work to determine a jointly optimal packet scheduler at the MAs and AS. We significantly reduce its computational complexity by iteratively solving a distributed optimization problem, independently at MAs and AS. Using Monte Carlo simulations, we show that the proposed joint packet scheduler outperforms other state-of-the-art packet schedulers and results in (desirable) near-minimal delay-jitter for delay-sensitive traffic at the expense of (tolerable) higher delay-jitter for delay-tolerant traffic.

Real-Time Data Center's Telemetry Reduction and Reconstruction Using Markov Chain Models

Large-scale data centers are composed of thousands of servers organized in interconnected racks to offer services to users. These data centers continuously generate large amounts of telemetry data streams (e.g., hardware utilization metrics) used for multiple purposes, including resource management, workload characterization, resource utilization prediction, capacity planning, and real-time analytics. These telemetry streams require costly bandwidth utilization and storage space, particularly at medium-long term for large data centers. This paper addresses this problem by proposing and evaluating a system to efficiently reduce bandwidth and storage for telemetry data through real-time modeling using Markov chain based methods. Our proposed solution was evaluated using real telemetry datasets and compared with polynomial regression methods for reducing and reconstructing data. Experimental results show that data can be lossy compressed up to 75% for bandwidth utilization and 95.33% for storage space, with reconstruction accuracy close to 92%.

ICER, ISPOR AND QALYs: Tales of Imaginary Worlds.

Previous commentaries in the Formulary Evaluation section of INNOVATIONS in Pharmacy have pointed to the lack of credibility in modeled claims for cost-effectiveness and associated recommendations for pricing by the Institute for Clinical and Economic Review (ICER). The principal objection to ICER reports has been that their modeled claims fail the standards of normal science: they are best seen as pseudoscience. The purpose of this latest commentary is twofold: first, to review the latest report by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) on standards and value claims for modeling imaginary cost-per-quality life year(QALY) worlds in health technology assessments and (ii) given ICER's commitment to ISPOR standards, to consider thecredibility of ICER's QALYs. The concern is that ICER gives the impression that there is a common and agreed utility metric to support QALY constructs and that there is an agreed standard for creating QALYs within the imaginary reference case meme. The purpose of this commentary is to emphasize that there is no 'gold standard' QALY, let alone a 'gold standard' utility metric. A QALY is what you assume the QALY should be; it is an artificial construct which allows any number of competing QALYs to be generated for the same target population under different imaginary constructs. Similar objections apply to the application of willingness-to-pay thresholds to support ICER's business case as the sole arbiter, in pole position, of health technology assessments to support pricing recommendations. In the US. A threshold has to be defined for the QALYs and costs captured, by assumption, within the model. Even for the same target patient population a $50,000 cost per QALY threshold will yield different pricing recommendations depending upon the assumptions driving the QALY estimate, such as choice of utility metric, time spent by stage of disease, frequency and severity of adverse events and costs. While this is an obvious point, it appears not to occur to those who, with a limited technical understanding of how the ICER model is constructed (and ICER is not given to extended technical explanations) take at face value the ICER imaginary construct and the QALY value judgements. Of course, it is also beside the point that the ICER reference case imaginary world was never intended to support empirical assessments of the claims made; we have no idea of whether it is right, if it is wrong and, over the lifetime perspective, we will never know and were never intended to know.

1054. Impact of Prospective Review and Feedback with Peer Comparison on Carbapenem Utilization by Physicians Practicing at a Community Teaching Hospital

BackgroundBehavioral interventions such as peer comparison have shown to reduce inappropriate antibiotic utilization in outpatient settings. We aimed to estimate the impact of prospective review and feedback with periodic peer comparison on carbapenem use by physicians in an inpatient setting.MethodsInterrupted time series study conducted at a 400-bed community teaching hospital with an Antimicrobial Stewardship Program (ASP) in place since 2012. Prospective review and feedback is the ASPs main strategy. Carbapenem use is not restricted. The intervention was limited to internal medicine residents, system-employed hospitalists, critical care specialists, surgery residents and surgery attendings directly supervising residents. Each carbapenem day of therapy (DOT) was reviewed by an infectious diseases (ID) physician or ID pharmacist and classified as adequate, suboptimal unnecessary or inappropriate. For the purposes of peer comparison, each DOT was attributed to the physician directly responsible for patient care on the day a carbapenem was administered. Among patients admitted to teaching services, both the resident and their supervising attending were deemed responsible. Individual physicians’ proportions of adequate use were calculated and compared with the aggregate proportion of adequate use by service, i.e., hospitalists were compared with other hospitalists. An email summarizing utilization metrics and comparing to their peers was sent on a monthly basis. The main outcome of interest was hospital-wide carbapenem use measured in DOT per thousand patient-days. Carbapenem DOT use by service was a secondary outcome. Changes in post-intervention trends were calculated as incidence rate ratios (IRR).ResultsFollowing the onset of the intervention there were no changes in hospital-wide trends of carbapenem use (IRR 1.04; 95% CI 0.98–1.10; P = 0.21) (Figure 1). Analysis of carbapenem use by service showed prescribing trends remained stable within services, with IRR in medical service of 0.98 (95% CI 0.92–1.05; P = 0.61) and IRR in the surgical service of 1.05 (95% CI 0.99–1.13; P = 0.11) (Figures 2 and 3). No changes were seen in proportions of adequate use.ConclusionAddition of peer comparison to an ASP utilizing prospective review and feedback did not decrease carbapenem use. Disclosures All authors: No reported disclosures.

153 Freestanding vs. Hospital-Based Emergency Department Utilization and Throughput Metrics Across the United States

Freestanding emergency departments (EDs) have grown significantly in the past decade and represent approximately 10% of EDs in the United States. The objective of this study was to compare throughput metrics and utilization measures for freestanding ED versus hospital-based EDs of similar volume and geographic region. ED Benchmarking Alliance data for freestanding EDs (FSED) and hospital-based EDs (HBED) for 2017 was utilized. Our explanatory variable was facility type (freestanding versus hospital-based ED). Outcomes between FSED and HBED were compared. Normally distributed variables are presented as mean and standard deviation and p-values were obtained from t-tests. Non-normally distributed variables are presented as median and interquartile range and p-values from Wilcoxon rank sum tests. Outcomes were weighted based on the proportion of ED volume per facility. Outcome measures between FSED and HBED were compared both nationally and at the state level. Overall, the FSED performed better in most outcomes at the National level as compared to HBED although there are some measures that the HBED performed better than the FSED. Median length of stay (104.2 vs. 140.0), length of stay for treated and released patients (98.6 vs. 122.9), door to bed (4 vs 8), door to doctor (11 vs 16), percentage of patients admitted through the ED (4 vs 11) and percentage of patients leaving the ED without being seen (LWBS) (0.9 vs 1.5), were significantly lower in the FSED as compared to HBED, (P<0.0001). On the other hand, length of stay for admitted patients (265.9 vs 241.8), and median boarding time (96.8 vs. 71.3) were significantly lower in HBED when compared to FSED (P<0.0001). X-ray utilization per 100 patients was 24.6 at the FSED and 37.7 at the HBED (P<0.001). CT utilization per 100 patients was 10.76 at the FSED and 16.02 at the HBED (P<0.0001). EKG utilization per 100 patients was 10.32 at the FSED and 19.06 at the HBED (P<0.0001). At the state level, median length of stay, door to doctor, door to bed and percentage of LWBS were lower in FSEDs than in HBEDs while length of stay to admission, length of stay to discharge, median boarding as well as other variables varied by state. FEDs compared to HBEDs of similar size and geographic location across the country have faster length of stay, door to doctor, door to bed and lower LWBS compared to HBEDs and lower CT, EKG and x-ray utilization.

A Novel Graph-modification Technique for User Privacy-preserving on Social Networks

The growing popularity of social networks and the increasing need for publishing related data mean that protection of privacy becomes an important and challenging problem in social networks. This paper describes the (k,l k,l k,l)-anonymity model used for social network graph anonymization. The method is based on edge addition and is utility-aware, i.e. it is designed to generate a graph that is similar to the original one. Different strategies are evaluated to this end and the results are compared based on common utility metrics. The outputs confirm that the na¨ıve idea of adding some random or even minimum number of possible edges does not always produce useful anonymized social network graphs, thus creating some interesting alternatives for graph anonymization techniques.

Variational approach for privacy funnel optimization on continuous data

Here we consider a common data encryption problem encountered by users who want to disclose some data to gain utility but preserve their private information. Specifically, we consider the inference attack, in which an adversary conducts inference on the disclosed data to gain information about users’ private data. Following privacy funnel (Makhdoumi et al., 2014), assuming that the original data X is transformed into Z before disclosing and the log loss is used for both privacy and utility metrics, then the problem can be modeled as finding a mapping X→Z that maximizes mutual information between X and Z subject to a constraint that the mutual information between Z and private data S is smaller than a predefined threshold ϵ. In contrast to the original study (Makhdoumi et al., 2014), which only focused on discrete data, we consider the more general and practical setting of continuous and high-dimensional disclosed data (e.g., image data). Most previous work on privacy-preserving representation learning is based on adversarial learning or generative adversarial networks, which has been shown to suffer from the vanishing gradient problem, and it is experimentally difficult to eliminate the relationship with private data Y when Z is constrained to retain more information about X. Here we propose a simple but effective variational approach that does not rely on adversarial training. Our experimental results show that our approach is stable and outperforms previous methods in terms of both downstream task accuracy and mutual information estimation.

Wearable cardioverter-defibrillators in pediatric cardiomyopathy: A cost-utility analysis

Dilated cardiomyopathy (DCM) is the most common cardiomyopathy in children. Patients with severe cardiac dysfunction are thought to be at risk of sudden cardiac arrest (SCA). After diagnosis, a period of medical optimization is recommended before permanent implantable cardioverter-defibrillator (ICD) implantation. Wearable cardioverter-defibrillators (WCDs) provide an option for arrhythmia protection as an outpatient during this optimization. The purpose of this study was to determine the strategy that optimizes cost and survival during medical optimization of a patient with DCM before ICD placement. A Markov state transition model was constructed for the 3 clinical approaches to compare costs, clinical outcomes, and quality of life: (1) "Inpatient," (2) "Home-WCD," and (3) "Home-No WCD." Transitional probabilities, costs, and utility metrics were extracted from the existing literature. Cost-effectiveness was assessed comparing each paradigm's incremental cost-effectiveness ratio against a societal willingness-to-pay threshold of $50,000 per quality-adjusted life year. The cost-utility analysis illustrated that Home-WCD met the willingness-to-pay threshold with an incremental cost-effectiveness ratio of $20,103 per quality-adjusted life year and 4 mortalities prevented per 100 patients as compared with Home-No WCD. One-way sensitivity analyses demonstrated that Home-No WCD became the most cost-effective solution when the probability of SCA fell below 0.2% per week, the probability of SCA survival with a WCD fell below 9.8%, or the probability of SCA survival with Home-No WCD quadrupled from base-case assumptions. Based on the existing literature probabilities of SCA in pediatric patients with DCM undergoing medical optimization before ICD implantation, sending a patient home with a WCD may be a cost-effective strategy.

Utility analysis on privacy-preservation algorithms for online social networks: an empirical study

Social networks have gained tremendous popularity recently. Millions of people use social network apps to share precious moments with friends and family. Users are often asked to provide personal information such as name, gender, and address when using social networks. However, as the social network data are collected, analyzed, and re-published at a large scale, personal information might be misused by unauthorized third parties and even attackers. Therefore, extensive research has been carried out to protect the data from privacy violations in social networks. The most popular technique is graph perturbation, which modifies the local topological structure of a social network user (a vertex) via various randomization techniques before the social graph data is published. Nevertheless, graph anonymization may affect the usability of the data as random noises are introduced, decreasing user experience. Therefore, a trade-off between privacy protection and data usability must be sought. In this paper, we employ various graph and application utility metrics to investigate this trade-off. More specifically, we conduct an empirical study by implementing five state-of-the-art anonymization algorithms to analyze the graph and application utilities on a Facebook and a Twitter dataset. Our results indicate that most anonymization algorithms can partially or conditionally preserve the graph and application utilities and any single anonymization algorithm may not always perform well on different datasets. Finally, drawing on the reviewed graph anonymization techniques, we provide a brief overview on future research directions and challenges involved therein.

Utilization Variation In Military Versus Civilian Care: Evidence From TRICARE

The Defense Health Agency was established five years ago to integrate and centralize the provision of health care that had been managed separately by the Army, Navy, and Air Force. One favored proposal is to increase the use of private-sector or civilian health care providers. This study compared geographic variation in health care use (a common proxy for efficiency) between patients with a military (direct care) system and those with a civilian (purchased care) system primary care provider-both of which are offered in TRICARE Prime, a health plan that resembles a health maintenance organization. We found similar levels of variation across care utilization metrics with the exception of specialty care, in which the military sample had less variation than its civilian counterpart did. In the military system, risk-adjusted utilization levels were substantially lower for primary care visits and higher for specialty care visits, compared to these visits under the civilian system. Our findings suggest that expanding the use of the civilian system might not achieve the desired efficiencies. Rather, focusing on specialty care in the military system and expanding primary care in the civilian system could help achieve operational readiness and enhanced efficiency.

Protein Energy Malnutrition Is Associated with Worse Outcomes in Sepsis—A Nationwide Analysis

BackgroundProtein-energy malnutrition (PEM), resulting from depleted energy and nutrient stores, compromises the body’s defense systems and may exacerbate sepsis and its impact. However, population-based studies examining the association of PEM on the prevalence and health-care burden of sepsis are lacking. ObjectiveTo investigate the relationship between PEM and sepsis, influence of PEM on clinical outcomes of sepsis, and impact of PEM on trends in sepsis mortality. DesignThe primary study is a retrospective cohort analysis of the 2012-2014 National Inpatient Sample (NIS) patient discharge records. Secondary analyses are cross-sectional study on the 2014 NIS and trend analysis on 2007-2014 NIS. Participants/settingThe primary study included adult inpatient hospitalizations for sepsis in the United States. Main outcome measuresMortality, complicated sepsis, and 10 other metrics of clinical outcomes and health care utilization. Statistical analysisFirst, patients with sepsis (2014 NIS) were stratified into two groups: uncomplicated (without shock) and complicated (with shock). The adjusted odds ratio of having sepsis (total, uncomplicated, and complicated) was estimated with PEM as predictor using logistic regressions (binomial and multinomial). Second, among patients with sepsis (2012-2014 NIS), PEM cases were matched to cases without PEM (no-PEM) using a greedy-algorithm based propensity-matching methodology (1:1), and the outcomes were measured with conditional regression models. Finally, the trend in mortality from sepsis was calculated, stratified by PEM status, as an effect modifier, using Poisson models (2007-2014 NIS). All models accounted for the complex sampling methodology (SAS 9.4). ResultsIn 2014, PEM was associated with higher odds for sepsis (3.97 [3.89 to 4.05], P<0.0001) and complicated vs uncomplicated sepsis (1.74 [1.67 to 1.81], P<0.0001). From 2012-2014, about 18% (167,133 of 908,552) of hospitalizations for sepsis had coexisting PEM. After propensity matching, PEM was associated with higher mortality (adjusted odds ratio: 1.35 [1.32 to 1.37], P<0.0001), cost ($160,724 [159,517 to 161,940] vs $86,650 [85,931 to 87,375], P<0.0001), length of stay (14.8 [14.9 to 14.8] vs 8.5 [8.5 to 8.6] days, P<0.0001), adverse events, and resource utilization. Although mortality in sepsis has been trending down from 2007-2014 (−1.19% per year, P trend<0.0001), the decrease was less pronounced among those with PEM vs no-PEM (−0.86% per year vs −1.29% per year, P<0.0001). ConclusionsPEM is a risk factor for sepsis and associated with poorer outcomes among patients with sepsis. A concerted effort involving all health care workers in the prevention, identification, and treatment of PEM in community-dwelling people before hospitalization might mitigate against these devastating outcomes.

Characterization of high healthcare utilizer groups using administrative data from an electronic medical record database

BackgroundHigh utilizers (HUs) are a small group of patients who impose a disproportionately high burden on the healthcare system due to their elevated resource use. Identification of persistent HUs is pertinent as interventions have not been effective due to regression to the mean in majority of patients. This study will use cost and utilization metrics to segment a hospital-based patient population into HU groups.MethodsThe index visit for each adult patient to an Academic Medical Centre in Singapore during 2006 to 2012 was identified. Cost, length of stay (LOS) and number of specialist outpatient clinic (SOC) visits within 1 year following the index visit were extracted and aggregated. Patients were HUs if they exceeded the 90th percentile of any metric, and Non-HU otherwise. Seven different HU groups and a Non-HU group were constructed. The groups were described in terms of cost and utilization patterns, socio-demographic information, multi-morbidity scores and medical history. Logistic regression compared the groups’ persistence as a HU in any group into the subsequent year, adjusting for socio-demographic information and diagnosis history.ResultsA total of 388,162 patients above the age of 21 were included in the study. Cost-LOS-SOC HUs had the highest multi-morbidity and persistence into the second year. Common conditions among Cost-LOS and Cost-LOS-SOC HUs were cardiovascular disease, acute cerebrovascular disease and pneumonia, while most LOS and LOS-SOC HUs were diagnosed with at least one mental health condition. Regression analyses revealed that HUs across all groups were more likely to persist compared to Non-HUs, with stronger relationships seen in groups with high SOC utilization. Similar trends remained after further adjustment.ConclusionHUs of healthcare services are a diverse group and can be further segmented into different subgroups based on cost and utilization patterns. Segmentation by these metrics revealed differences in socio-demographic characteristics, disease profile and persistence. Most HUs did not persist in their high utilization, and high SOC users should be prioritized for further longitudinal analyses. Segmentation will enable policy makers to better identify the diverse needs of patients, detect gaps in current care and focus their efforts in delivering care relevant and tailored to each segment.

Average Weighted Accuracy: Pragmatic Analysis for a Rapid Diagnostics in Categorizing Acute Lung Infections (RADICAL) Study.

Patient management relies on diagnostic information to identify appropriate treatment. Standard evaluations of diagnostic tests consist of estimating sensitivity, specificity, positive/negative predictive values, likelihood ratios, and accuracy. Although useful, these metrics do not convey the tests' clinical value, which is critical to informing decision-making. Full appreciation of the clinical impact of a diagnostic test requires analyses that integrate sensitivity and specificity, account for the disease prevalence within the population of test application, and account for the relative importance of specificity vs sensitivity by considering the clinical implications of false-positive and false-negative results. We developed average weighted accuracy (AWA), representing a pragmatic metric of diagnostic yield or global utility of a diagnostic test. AWA can be used to compare test alternatives, even across different studies. We apply the AWA methodology to evaluate a new diagnostic test developed in the Rapid Diagnostics in Categorizing Acute Lung Infections (RADICAL) study.

Temporal Changes in Health Care Utilization among Participants of a Medically Supervised Weight Management Program.

Obesity is associated with increased incidence of chronic diseases such as type 2 diabetes mellitus, systemic hypertension, and other risk factors for cardiovascular disease. Obesity is also associated with increased use of outpatient clinical services, a metric of health care utilization. However, little is known of temporal changes in health care utilization among obese participants of a medical weight management program. To assess changes in 3 health care utilization metrics (primary care physician office visits, ambulatory clinic office visits, and health care touches [encounters]) in weight management program participants across 21 Kaiser Permanente Northern California Medical Centers during a 5-year period. Retrospective observational study of 10,693 participants, with a linear-mixed effects model to account for repeated-measures analysis. Five-year temporal changes in the 3 health care metrics. At baseline, the participants' average age (standard deviation) was 51.1 (12.4) years, and their mean body mass index (standard deviation) was 39.7 (7.2) kg/m2. At the end of 4 months, there was a decrease in primary care visits (p < 0.001), with an increase in ambulatory clinic visits and health care touches (p < 0.001), because of increased weight management visits. At the end of 5 years, there was a 25% to 35% decrease from baseline in all 3 health care utilization metrics (p < 0.0001). Although slightly attenuated, these findings were similar in a risk-adjusted model. Our findings may be useful to other integrated health care delivery systems considering initiating a similar weight management program.

Basic, Advanced, and Novel Metrics to Guide Antibiotic Use Assessments

This review illustrates relevant and current antibiotic use (AU) metrics and demonstrates their differences. The authors also highlight novel, increasingly complex AU metrics proposed for use in antibiotic stewardship research. Many common antibiotic use metrics (e.g., days of therapy, length of therapy) are well defined, widely generalizable, and commonly used by antimicrobial stewardship programs. Multiple measures are required to comprehensively capture real-world AU, though utilization metrics often lack important contextual information needed to inform antibiotic stewardship strategy. Combining standard AU metrics with meaningful characterization of antibiotic prescriptions, including appropriateness of therapy or antibiotic spectrum, provides important information for developing strategy. Current antibiotic use metrics are limited by an inability to convey meaningful context. Future research should aim to define generalizable measures that illustrate improved prescribing in addition to volume of use.

Metrics of clonidine utilization in maintenance hemodialysis patients

Background: Clonidine is less frequently used by nephrologists. Data on clonidine prescribing trends in hemodialysis patients is sparse. We assessed the clonidine utilization metrics from the case records of patients undergoing maintenance hemodialysis.Methods: In this retrospective chart review, we analysed the clinical records of hemodialysis patients using clonidine. We evaluated the frequency of clonidine use, mean dose of clonidine and percentage of patients receiving a particular dose. Additionally, we also correlated dose of clonidine with anti-hypertensive pill count.Results: A total of 70 hemodialysis patients case records were screened. All 70/70 (100%) of them were hypertensive. Only 25/70 (35.74%) of patients were prescribed clonidine as an anti-hypertensive agent. The mean clonidine dose was 352±171 µg. Majority of patients 9/25 (36%) received 400 µg of clonidine. The dose of clonidine was prescribed in the order 400 µg (36%)&gt;200 µg (32%)&gt;600 µg (16%)&gt;100 µg (8%)&gt;300 µg (4%)=700 µg (4%). There was a statistically significant correlation in the strength of clonidine prescribed with increasing anti-hypertensive drugs (p&lt;0.05).Conclusions: In our study, we observed that 80% of our hemodialysis patients were non responders to either systolic blood pressure or diastolic blood pressure or both. Oral clonidine use was observed in 35.74% of our hemodialysis patients. There was a linear trend showing an increased dose of clonidine with an increase in the anti-hypertensive pill count.

Teamwork Among Medicine House Staff During Work Rounds: Development of a Direct Observation Tool.

Teamwork is integral to effective health care but difficult to evaluate. Few tools have been tested outside of classroom or medical simulation settings. Accordingly, we aimed to develop and pilot test an easy-to-use direct observation instrument for measuring teamwork among medical house staff. We performed direct observations of 18 inpatient medicine house staff teams at a teaching hospital using an instrument constructed from existing teamwork tools, expert panel review, and pilot testing. We examined differences across teams using the Kruskal-Wallis statistic. We examined interrater reliability with the κ statistic, domain scales using Cronbach α, and construct validity using correlation and multivariable regression analyses of quality and utilization metrics. Observers rated team performance before and after providing feedback to 12 of the 18 team leaders and assessed changes in team performance using paired two-tailed t tests. We found variation in team performance in the situation monitoring, mutual support, and communication domains. The instrument evidenced good interrater reliability among concurrent, independent observers (κ = 0.7, P < 0.001). It had satisfactory face validity based on expert panel review and the assessments of resident team leaders. Construct validity was supported by a positive correlation between team performance and the Hospital Consumer Assessment of Healthcare Providers and Systems physician communication score (r = 0.6, P = 0.03). Providing resident physicians with information about their teams' performance was associated with improved mean performance in follow-up observations (3.6-3.8/4.0, P = 0.001). Direct observation of teamwork behaviors by medicine house staff on ward rounds is feasible and feedback may improve performance.