The Experts below are selected from a list of 177 Experts worldwide ranked by ideXlab platform
Linda S. Williams - One of the best experts on this subject based on the ideXlab platform.
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Validation of Stroke Meaningful Use Measures in a National Electronic Health Record System
Journal of General Internal Medicine, 2016Co-Authors: Michael S. Phipps, Jeff Fahner, Danielle Sager, Jessica Coffing, Bailey Maryfield, Linda S. WilliamsAbstract:Background The Meaningful Use (MU) program has increased the national emphasis on electronic measurement of hospital quality. Objective To evaluate stroke MU and one VHA stroke electronic clinical quality measure (eCQM) in national VHA Data and determine sources of error in using Centralized electronic health record (EHR) Data. Design Our study is a retrospective cross-sectional study of stroke quality measure eCQMs vs. chart review in a national EHR. We developed local SQL algorithms to generate the eCQMs, then modified them to run on VHA Central Data Warehouse (CDW) Data. eCQM results were generated from CDW Data in 2130 ischemic stroke admissions in 11 VHA hospitals. Local and CDW results were compared to chart review. Main Measures We calculated the raw proportion of matching cases, sensitivity/specificity, and positive/negative predictive values (PPV/NPV) for the numerators and denominators of each eCQM. To assess overall agreement for each eCQM, we calculated a weighted kappa and prevalence-adjusted bias-adjusted kappa statistic for a three-level outcome: ineligible, eligible-passed, or eligible-failed. Key Results In five eCQMs, the proportion of matched cases between CDW and chart ranged from 95.4 %–99.7 % (denominators) and 87.7 %–97.9 % (numerators). PPVs tended to be higher (range 96.8 %–100 % in CDW) with NPVs less stable and lower. Prevalence-adjusted bias-adjusted kappas for overall agreement ranged from 0.73–0.95. Common errors included difficulty in identifying: (1) mechanical VTE prophylaxis devices, (2) hospice and other specific discharge disposition, and (3) contraindications to receiving care processes. Conclusions Stroke MU indicators can be relatively accurately generated from existing EHR systems (nearly 90 % match to chart review), but accuracy decreases slightly in Central compared to local Data sources. To improve stroke MU measure accuracy, EHRs should include standardized Data elements for devices, discharge disposition (including hospice and comfort care status), and recording contraindications.
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Validation of Stroke Meaningful Use Measures in a National Electronic Health Record System
Journal of General Internal Medicine, 2016Co-Authors: Michael S. Phipps, Jeff Fahner, Danielle Sager, Jessica Coffing, Bailey Maryfield, Linda S. WilliamsAbstract:The Meaningful Use (MU) program has increased the national emphasis on electronic measurement of hospital quality. To evaluate stroke MU and one VHA stroke electronic clinical quality measure (eCQM) in national VHA Data and determine sources of error in using Centralized electronic health record (EHR) Data. Our study is a retrospective cross-sectional study of stroke quality measure eCQMs vs. chart review in a national EHR. We developed local SQL algorithms to generate the eCQMs, then modified them to run on VHA Central Data Warehouse (CDW) Data. eCQM results were generated from CDW Data in 2130 ischemic stroke admissions in 11 VHA hospitals. Local and CDW results were compared to chart review. We calculated the raw proportion of matching cases, sensitivity/specificity, and positive/negative predictive values (PPV/NPV) for the numerators and denominators of each eCQM. To assess overall agreement for each eCQM, we calculated a weighted kappa and prevalence-adjusted bias-adjusted kappa statistic for a three-level outcome: ineligible, eligible-passed, or eligible-failed. In five eCQMs, the proportion of matched cases between CDW and chart ranged from 95.4 %–99.7 % (denominators) and 87.7 %–97.9 % (numerators). PPVs tended to be higher (range 96.8 %–100 % in CDW) with NPVs less stable and lower. Prevalence-adjusted bias-adjusted kappas for overall agreement ranged from 0.73–0.95. Common errors included difficulty in identifying: (1) mechanical VTE prophylaxis devices, (2) hospice and other specific discharge disposition, and (3) contraindications to receiving care processes. Stroke MU indicators can be relatively accurately generated from existing EHR systems (nearly 90 % match to chart review), but accuracy decreases slightly in Central compared to local Data sources. To improve stroke MU measure accuracy, EHRs should include standardized Data elements for devices, discharge disposition (including hospice and comfort care status), and recording contraindications.
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Abstract W P275: Validation of Stroke Meaningful Use Measures in a National EHR System
Stroke, 2015Co-Authors: Michael S. Phipps, Jeff Fahner, Danielle Sager, Jessica Coffing, Bailey Maryfield, Linda S. WilliamsAbstract:Background: The Affordable Care Act included eight stroke indicators in its Meaningful Use (MU) program. This project evaluated stroke MU measures in national VHA Data and determined sources of error in using Centralized electronic health record (EHR) Data. Methods: We converted local SQL queries that generated stroke indicators to run on VA Central Data Warehouse (CDW) Data, mapping each local Data element to the corresponding CDW Data element and table. Numerator (NM) and denominator (DN) results were generated from CDW Data in a sample of 2200 ischemic stroke admissions in 11 VA hospitals. Local and CDW NM and DN results were compared to chart review. NM and DN mismatch reports were iteratively examined to identify, categorize, and correct sources of error. We calculated passing rates, sensitivity and specificity for the NMs and DNs, and an overall accuracy kappa statistic. Results: Results for two measures (VTE prophylaxis and antithrombotic (AT) by day 2) are shown in the Table. The most common error in VTE prophylaxis was failure to identify mechanical prophylaxis devices (171/185 NM false negative errors), and in the AT measure was failure to identify a contraindication to therapy (50/59 DN false positive errors). Errors impacting multiple indicators included difficulty identifying Comfort Care status, discrepancies between electronic and charted medication administration, and difficulty identifying medications given in the ER. Passing rates (chart review vs. EHR) were higher with chart review for VTE (87% vs 76%) but similar for AT (91% vs 90%). Conclusions: Stroke MU indicators can be relatively accurately generated from existing EHR systems but accuracy decreases in Central compared to local Data sources. To improve stroke MU measure accuracy, EHRs should include standardized Data elements for devices, Comfort Care status, recording contraindications, and medications given in the ER.
Michael S. Phipps - One of the best experts on this subject based on the ideXlab platform.
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Validation of Stroke Meaningful Use Measures in a National Electronic Health Record System
Journal of General Internal Medicine, 2016Co-Authors: Michael S. Phipps, Jeff Fahner, Danielle Sager, Jessica Coffing, Bailey Maryfield, Linda S. WilliamsAbstract:Background The Meaningful Use (MU) program has increased the national emphasis on electronic measurement of hospital quality. Objective To evaluate stroke MU and one VHA stroke electronic clinical quality measure (eCQM) in national VHA Data and determine sources of error in using Centralized electronic health record (EHR) Data. Design Our study is a retrospective cross-sectional study of stroke quality measure eCQMs vs. chart review in a national EHR. We developed local SQL algorithms to generate the eCQMs, then modified them to run on VHA Central Data Warehouse (CDW) Data. eCQM results were generated from CDW Data in 2130 ischemic stroke admissions in 11 VHA hospitals. Local and CDW results were compared to chart review. Main Measures We calculated the raw proportion of matching cases, sensitivity/specificity, and positive/negative predictive values (PPV/NPV) for the numerators and denominators of each eCQM. To assess overall agreement for each eCQM, we calculated a weighted kappa and prevalence-adjusted bias-adjusted kappa statistic for a three-level outcome: ineligible, eligible-passed, or eligible-failed. Key Results In five eCQMs, the proportion of matched cases between CDW and chart ranged from 95.4 %–99.7 % (denominators) and 87.7 %–97.9 % (numerators). PPVs tended to be higher (range 96.8 %–100 % in CDW) with NPVs less stable and lower. Prevalence-adjusted bias-adjusted kappas for overall agreement ranged from 0.73–0.95. Common errors included difficulty in identifying: (1) mechanical VTE prophylaxis devices, (2) hospice and other specific discharge disposition, and (3) contraindications to receiving care processes. Conclusions Stroke MU indicators can be relatively accurately generated from existing EHR systems (nearly 90 % match to chart review), but accuracy decreases slightly in Central compared to local Data sources. To improve stroke MU measure accuracy, EHRs should include standardized Data elements for devices, discharge disposition (including hospice and comfort care status), and recording contraindications.
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Validation of Stroke Meaningful Use Measures in a National Electronic Health Record System
Journal of General Internal Medicine, 2016Co-Authors: Michael S. Phipps, Jeff Fahner, Danielle Sager, Jessica Coffing, Bailey Maryfield, Linda S. WilliamsAbstract:The Meaningful Use (MU) program has increased the national emphasis on electronic measurement of hospital quality. To evaluate stroke MU and one VHA stroke electronic clinical quality measure (eCQM) in national VHA Data and determine sources of error in using Centralized electronic health record (EHR) Data. Our study is a retrospective cross-sectional study of stroke quality measure eCQMs vs. chart review in a national EHR. We developed local SQL algorithms to generate the eCQMs, then modified them to run on VHA Central Data Warehouse (CDW) Data. eCQM results were generated from CDW Data in 2130 ischemic stroke admissions in 11 VHA hospitals. Local and CDW results were compared to chart review. We calculated the raw proportion of matching cases, sensitivity/specificity, and positive/negative predictive values (PPV/NPV) for the numerators and denominators of each eCQM. To assess overall agreement for each eCQM, we calculated a weighted kappa and prevalence-adjusted bias-adjusted kappa statistic for a three-level outcome: ineligible, eligible-passed, or eligible-failed. In five eCQMs, the proportion of matched cases between CDW and chart ranged from 95.4 %–99.7 % (denominators) and 87.7 %–97.9 % (numerators). PPVs tended to be higher (range 96.8 %–100 % in CDW) with NPVs less stable and lower. Prevalence-adjusted bias-adjusted kappas for overall agreement ranged from 0.73–0.95. Common errors included difficulty in identifying: (1) mechanical VTE prophylaxis devices, (2) hospice and other specific discharge disposition, and (3) contraindications to receiving care processes. Stroke MU indicators can be relatively accurately generated from existing EHR systems (nearly 90 % match to chart review), but accuracy decreases slightly in Central compared to local Data sources. To improve stroke MU measure accuracy, EHRs should include standardized Data elements for devices, discharge disposition (including hospice and comfort care status), and recording contraindications.
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Abstract W P275: Validation of Stroke Meaningful Use Measures in a National EHR System
Stroke, 2015Co-Authors: Michael S. Phipps, Jeff Fahner, Danielle Sager, Jessica Coffing, Bailey Maryfield, Linda S. WilliamsAbstract:Background: The Affordable Care Act included eight stroke indicators in its Meaningful Use (MU) program. This project evaluated stroke MU measures in national VHA Data and determined sources of error in using Centralized electronic health record (EHR) Data. Methods: We converted local SQL queries that generated stroke indicators to run on VA Central Data Warehouse (CDW) Data, mapping each local Data element to the corresponding CDW Data element and table. Numerator (NM) and denominator (DN) results were generated from CDW Data in a sample of 2200 ischemic stroke admissions in 11 VA hospitals. Local and CDW NM and DN results were compared to chart review. NM and DN mismatch reports were iteratively examined to identify, categorize, and correct sources of error. We calculated passing rates, sensitivity and specificity for the NMs and DNs, and an overall accuracy kappa statistic. Results: Results for two measures (VTE prophylaxis and antithrombotic (AT) by day 2) are shown in the Table. The most common error in VTE prophylaxis was failure to identify mechanical prophylaxis devices (171/185 NM false negative errors), and in the AT measure was failure to identify a contraindication to therapy (50/59 DN false positive errors). Errors impacting multiple indicators included difficulty identifying Comfort Care status, discrepancies between electronic and charted medication administration, and difficulty identifying medications given in the ER. Passing rates (chart review vs. EHR) were higher with chart review for VTE (87% vs 76%) but similar for AT (91% vs 90%). Conclusions: Stroke MU indicators can be relatively accurately generated from existing EHR systems but accuracy decreases in Central compared to local Data sources. To improve stroke MU measure accuracy, EHRs should include standardized Data elements for devices, Comfort Care status, recording contraindications, and medications given in the ER.
Frederick J. Angulo - One of the best experts on this subject based on the ideXlab platform.
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An association between decreasing incidence of invasive non-typhoidal salmonellosis and increased use of antiretroviral therapy, Gauteng Province, South Africa, 2003-2013.
PloS one, 2017Co-Authors: Karen H. Keddy, Simbarashe Takuva, Alfred Musekiwa, Adrian Puren, Arvinda Sooka, Alan S. Karstaedt, Keith P. Klugman, Frederick J. AnguloAbstract:Background HIV-infected persons are at increased risk of opportunistic infections, including invasive nontyphoidal Salmonella (iNTS) infections; antiretroviral therapy (ART) reduces this risk. We explored changing iNTS incidence associated with increasing ART availability in South Africa. Methods Laboratory-based surveillance for iNTS was conducted in Gauteng Province, South Africa, with verification using the National Health Laboratory Service’s Central Data Warehouse (CDW), between 2003 and 2013. Isolates were serotyped at the Centre for Enteric Diseases. CDW Data on patient numbers obtaining HIV viral load measurements provided estimates of numbers of HIV-infected patients receiving ART. A Poisson regression model was used to measure the changing incidence of iNTS infection from 2003 to 2013. The correlation between the incidence of iNTS and ART use from 2004 to 2013 was determined using Pearson’s correlation coefficient. Results From 2003–2013, the incidence of iNTS per 100,000 population per year decreased from 5.0 to 2.2 (p < .001). From 2004 to 2013, the incidence per 100,000 population of HIV viral load testing increased from 75.2 to 3,620.3 (p < .001). The most common serotypes causing invasive disease were Salmonella enterica serovar Typhimurium (Salmonella Typhimurium), and Salmonella Enteritidis: 2,469 (55.4%) and 1,156 (25.9%) of 4,459 isolates serotyped, respectively. A strong negative correlation was observed between decreasing iNTS incidence and increasing ART use from 2004 to 2013 (r = -0.94, p < .001). Similarly, decreasing incidence of invasive Salmonella Typhimurium infection correlated with increasing ART use (r = -0.93, p < .001). Incidence of invasive Salmonella Enteritidis infection increased, however (r = 0.95, p < .001). Between 2003 and 2004, fewer adult men than women presented with iNTS (male-to-female rate ratio 0.73 and 0.89, respectively). This was reversed from 2005 through 2013 (ranging from 1.07 in 2005 to 1.44 in 2013). Adult men accessed ART less (male-to-female rate ratio ranging from 0.61 [2004] to 0.67 [2013]). Conclusions The incidence of iNTS infections including Salmonella Typhimurium decreased significantly in Gauteng Province in association with increased ART utilization. Adult men accessed ART programs less than women, translating into increasing iNTS incidence in this group. Monitoring iNTS incidence may assist in monitoring the ART program. Increasing incidence of invasive Salmonella Enteritidis infections needs further elucidation.
Ramesh K. Sitaraman - One of the best experts on this subject based on the ideXlab platform.
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Optimizing Timeliness and Cost in Geo-Distributed Streaming Analytics
IEEE Transactions on Cloud Computing, 2020Co-Authors: Benjamin Heintz, Abhishek Chandra, Ramesh K. SitaramanAbstract:Rapid Data streams are generated continuously from diverse sources including users, devices, and sensors located around the globe. This results in the need for efficient geo-distributed streaming analytics to extract timely information. A typical geo-distributed analytics service uses a hub-and-spoke model, comprising multiple edges connected by a wide-area-network (WAN) to a Central Data Warehouse. In this paper, we focus on the widely used primitive of windowed grouped aggregation , and examine the question of how much computation should be performed at the edges versus the center . We develop algorithms to optimize two key metrics: WAN traffic and staleness (delay in getting results). We present a family of optimal offline algorithms that jointly minimize these metrics, and we use these to guide our design of practical online algorithms based on the insight that windowed grouped aggregation can be modeled as a caching problem where the cache size varies over time. We evaluate our algorithms through an implementation in Apache Storm deployed on PlanetLab. Using workloads derived from anonymized traces of a popular analytics service from a large commercial CDN, our experiments show that our online algorithms achieve near-optimal traffic and staleness for a variety of system configurations, stream arrival rates, and queries.
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HPDC - Optimizing Grouped Aggregation in Geo-Distributed Streaming Analytics
Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing, 2015Co-Authors: Benjamin Heintz, Abhishek Chandra, Ramesh K. SitaramanAbstract:Large quantities of Data are generated continuously over time and from disparate sources such as users, devices, and sensors located around the globe. This results in the need for efficient geo-distributed streaming analytics to extract timely information. A typical analytics service in these settings uses a simple hub-and-spoke model, comprising a single Central Data Warehouse and multiple edges connected by a wide-area network (WAN). A key decision for a geo-distributed streaming service is how much of the computation should be performed at the edge versus the center. In this paper, we examine this question in the context of windowed grouped aggregation, an important and widely used primitive in streaming queries. Our work is focused on designing aggregation algorithms to optimize two key metrics of any geo-distributed streaming analytics service: WAN traffic and staleness (the delay in getting the result). Towards this end, we present a family of optimal offline algorithms that jointly minimize both staleness and traffic. Using this as a foundation, we develop practical online aggregation algorithms based on the observation that grouped aggregation can be modeled as a caching problem where the cache size varies over time. This key insight allows us to exploit well known caching techniques in our design of online aggregation algorithms. We demonstrate the practicality of these algorithms through an implementation in Apache Storm, deployed on the PlanetLab testbed. The results of our experiments, driven by workloads derived from anonymized traces of a popular web analytics service offered by a large commercial CDN, show that our online aggregation algorithms perform close to the optimal algorithms for a variety of system configurations, stream arrival rates, and query types.
Ishfaq Ahmad - One of the best experts on this subject based on the ideXlab platform.
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Policies for Caching OLAP Queries in Internet Proxies
IEEE Transactions on Parallel and Distributed Systems, 2006Co-Authors: T. Loukopoulps, Ishfaq AhmadAbstract:The Internet now offers more than just simple information to the users. Decision makers can now issue analytical, as opposed to transactional, queries that involve massive Data (such as, aggregations of millions of rows in a relational Database) in order to identify useful trends and patterns. Such queries are often referred to as Online-analytical processing (OLAP). Typically, pages carrying query results do not exhibit temporal locality and, therefore, are not considered for caching at Internet proxies. In OLAP processing, this is a major problem as the cost of these queries is significantly larger than that of the transactional queries. This paper proposes a technique to reduce the response time for OLAP queries originating from geographically distributed private LANs and issued through the Web toward a Central Data Warehouse (DW) of an enterprise. An active caching scheme is introduced that enables the LAN proxies to cache some parts of the Data, together with the semantics of the DW, in order to process queries and construct the resulting pages. OLAP queries arriving at the proxy are either satisfied locally or from the DW, depending on the relative access costs. We formulate a cost model for characterizing the respective latencies, taking into consideration the combined effects of both common Web access and query processing. We propose a cache admittance and replacement algorithm that operates on a hybrid Web-OLAP input, outperforming both pure-Web and pure-OLAP caching schemes
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ICPP - Active caching of on-line-analytical-processing queries in WWW proxies
International Conference on Parallel Processing 2001., 2001Co-Authors: Thanasis Loukopoulos, Panos Kalnis, Ishfaq Ahmad, Dimitris PapadiasAbstract:The Internet is offering more than just regular Web pages to the users. Decision makers can now issue analytical, as opposed to transactional, queries that involve massive Data (such as, aggregations of millions of rows in a relational Database) in order to identify useful trends and patterns. Such queries are referred to as On-Line-Analytical-Processing (OLAP) queries. Typically, pages carrying query results do not exhibit temporal locality and, therefore, are not considered for caching at WWW proxies. In OLAP processing, this becomes a major hurdle as the cost of such queries is much higher than traditional transactional queries. This paper proposes a systematic technique to reduce the response time for OLAP queries originating from geographically distributed private LANs and issued through the Web towards the Central Data Warehouse (DW) of an enterprise. An active caching scheme is proposed that enables the LAN proxies to cache some parts of the Data, together with the semantics of the DW in order to process queries and construct the resulting pages. OLAP queries arriving at the proxy are either satisfied locally or from the DW, depending on the relative access costs. We formulate a cost model for characterizing the latencies of these queries, taking into consideration normal Web access as well as analytical processing. We propose a cache admittance and replacement algorithm that outperforms a widely accepted caching algorithm.