11 resultados para decision support systems, GIS, interpolation, multiple regression

em DigitalCommons@The Texas Medical Center


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The purpose of this study was twofold: (1) To describe the relation of the intensity of DSS implementation to financial performance as an empirical exploration of improved performance at the organizational level. (2) To describe the relation of the intensity of DSS implementation to the type of organizational decision culture. A multiple case study design was utilized to compare three groups of paired cases. A pattern matching strategy was applied in this study. Four predictions were specified and compared to the empirical data. A progressively upward trend in the scores was predicted for the following theoretical relationships. (1) The greater the number of DSSs, the higher the sophistication index. (2) The greater the number of DSSs, the higher the financial ratios. (3) The greater the number of DSSs, the higher the culture score. (4) The higher the culture score, the higher the financial ratios. The data did not support any of the predicted trends except the relation between the number of DSSs and the financial ratios. The Income/Revenue ratio indicates the efficiency of a company's operations. One would expect that this ratio would be most affected by the operational and financial decision support systems. The majority of the systems measured in the study supported decisions tangential to the patient service areas. The evidence suggested that the type and number of decision support systems affects the bottom line. ^

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BACKGROUND: The most effective decision support systems are integrated with clinical information systems, such as inpatient and outpatient electronic health records (EHRs) and computerized provider order entry (CPOE) systems. Purpose The goal of this project was to describe and quantify the results of a study of decision support capabilities in Certification Commission for Health Information Technology (CCHIT) certified electronic health record systems. METHODS: The authors conducted a series of interviews with representatives of nine commercially available clinical information systems, evaluating their capabilities against 42 different clinical decision support features. RESULTS: Six of the nine reviewed systems offered all the applicable event-driven, action-oriented, real-time clinical decision support triggers required for initiating clinical decision support interventions. Five of the nine systems could access all the patient-specific data items identified as necessary. Six of the nine systems supported all the intervention types identified as necessary to allow clinical information systems to tailor their interventions based on the severity of the clinical situation and the user's workflow. Only one system supported all the offered choices identified as key to allowing physicians to take action directly from within the alert. Discussion The principal finding relates to system-by-system variability. The best system in our analysis had only a single missing feature (from 42 total) while the worst had eighteen.This dramatic variability in CDS capability among commercially available systems was unexpected and is a cause for concern. CONCLUSIONS: These findings have implications for four distinct constituencies: purchasers of clinical information systems, developers of clinical decision support, vendors of clinical information systems and certification bodies.

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Health care providers face the problem of trying to make decisions with inadequate information and also with an overload of (often contradictory) information. Physicians often choose treatment long before they know which disease is present. Indeed, uncertainty is intrinsic to the practice of medicine. Decision analysis can help physicians structure and work through a medical decision problem, and can provide reassurance that decisions are rational and consistent with the beliefs and preferences of other physicians and patients. ^ The primary purpose of this research project is to develop the theory, methods, techniques and tools necessary for designing and implementing a system to support solving medical decision problems. A case study involving “abdominal pain” serves as a prototype for implementing the system. The research, however, focuses on a generic class of problems and aims at covering theoretical as well as practical aspects of the system developed. ^ The main contributions of this research are: (1) bridging the gap between the statistical approach and the knowledge-based (expert) approach to medical decision making; (2) linking a collection of methods, techniques and tools together to allow for the design of a medical decision support system, based on a framework that involves the Analytic Network Process (ANP), the generalization of the Analytic Hierarchy Process (AHP) to dependence and feedback, for problems involving diagnosis and treatment; (3) enhancing the representation and manipulation of uncertainty in the ANP framework by incorporating group consensus weights; and (4) developing a computer program to assist in the implementation of the system. ^

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This study analyzed the relationship of family support systems and adolescent pregnancy outcomes. The population for the study was 390 adolescents who had attended the Marion County Health Department Adolescent Family Life Project in Indianapolis, Indiana during a two-year period.^ The study is unique in that it afforded the opportunity to compare adolescent pregnancy-related characteristics, of white and non-white adolescents in the same study.^ The pregnancy outcomes studied were: Infant birthweight, school attendance, and pregnancy recidivism.^ Significant results were found in the analysis that supported other research in regard to factors that are associated with school attendance when family support, adolescent's age, and ethnicity were controlled. Infant birthweight and repeat pregnancy outcome relationships were not found to have any consistently significant relationship with independent variables anticipated to be associated. However, the comparisons of infant birthweight among the adolescents with, and without, family support, by ethnicity resulted in some interesting findings. Repeat pregnancy proved an enigma, in that there seemed to be almost no variables in this study that were associated with the adolescent having a repeat pregnancy.^ Familial support in this study seemed to be of less importance as a factor in adolescent pregnancy outcomes than was ethnicity. The non-white adolescents in this study had a better record for remaining in school, both those non-white adolescents who lived with parents, and those who did not live with parents. More low birthweight occurred in the non-white adolescent, both those adolescents who lived with parents, and those who did not live with parents. Repeat pregnancy occurred more in the non-white adolescent whether she lived with parents, or did not live with parents. ^

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Errors in the administration of medication represent a significant loss of medical resources and pose life altering or life threatening risks to patients. This paper considered the question, what impact do Computerized Physician Order Entry (CPOE) systems have on medication errors in the hospital inpatient environment? Previous reviews have examined evidence of the impact of CPOE on medication errors, but have come to ambiguous conclusions as to the impact of CPOE and decision support systems (DSS). Forty-three papers were identified. Thirty-one demonstrated a significant reduction in prescribing error rates for all or some drug types; decreases in minor errors were most often reported. Several studies reported increases in the rate of duplicate orders and failures to remove contraindicated drugs, often attributed to inappropriate design or to an inability to operate the system properly. The evidence on the effectiveness of CPOE to reduce errors in medication administration is compelling though it is limited by modest study sample sizes and designs. ^

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In the demanding environment of healthcare reform, reduction of unwanted physician practice variation is promoted, often through evidence-based guidelines. Guidelines represent innovations that direct change(s) in physician practice; however, compliance has been disappointing. Numerous studies have analyzed guideline development and dissemination, while few have evaluated the consequences of guideline adoption. The primary purpose of this study was to explore and analyze the relationship between physician adoption of the glycated hemoglobin test guideline for management of adult patients with diabetes, and the cost of medical care. The study also examined six personal and organizational characteristics of physicians and their association with innovativeness, or adoption of the guideline. ^ Cost was represented by approved charges from a managed care claims database. Total cost, and diabetes and related complications cost, first were compared for all patients of adopter physicians with those of non-adopter physicians. Then, data were analyzed controlling for disease severity based on insulin dependency, and for high cost cases. There was no statistically significant difference in any of eight cost categories analyzed. This study represented a twelve-month period, and did not reflect cost associated with future complications known to result from inadequate management of glycemia. Guideline compliance did not increase annual cost, which, combined with the future benefit of glycemic control, lends support to the cost effectiveness of the guideline in the long term. Physician adoption of the guideline was recommended to reduce the future personal and economic burden of this chronic disease. ^ Only half of physicians studied had adopted the glycated hemoglobin test guideline for at least 75% of their diabetic patients. No statistically significant relationship was found between any physician characteristic and guideline adoption. Instead, it was likely that the innovation-decision process and guideline dissemination methods were most influential. ^ A multidisciplinary, multi-faceted approach, including interventions for each stage of the innovation-decision process, was proposed to diffuse practice guidelines more effectively. Further, it was recommended that Organized Delivery Systems expand existing administrative databases to include clinical information, decision support systems, and reminder mechanisms, to promote and support physician compliance with this and other evidence-based guidelines. ^

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BACKGROUND: We have carried out an extensive qualitative research program focused on the barriers and facilitators to successful adoption and use of various features of advanced, state-of-the-art electronic health records (EHRs) within large, academic, teaching facilities with long-standing EHR research and development programs. We have recently begun investigating smaller, community hospitals and out-patient clinics that rely on commercially-available EHRs. We sought to assess whether the current generation of commercially-available EHRs are capable of providing the clinical knowledge management features, functions, tools, and techniques required to deliver and maintain the clinical decision support (CDS) interventions required to support the recently defined "meaningful use" criteria. METHODS: We developed and fielded a 17-question survey to representatives from nine commercially available EHR vendors and four leading internally developed EHRs. The first part of the survey asked basic questions about the vendor's EHR. The second part asked specifically about the CDS-related system tools and capabilities that each vendor provides. The final section asked about clinical content. RESULTS: All of the vendors and institutions have multiple modules capable of providing clinical decision support interventions to clinicians. The majority of the systems were capable of performing almost all of the key knowledge management functions we identified. CONCLUSION: If these well-designed commercially-available systems are coupled with the other key socio-technical concepts required for safe and effective EHR implementation and use, and organizations have access to implementable clinical knowledge, we expect that the transformation of the healthcare enterprise that so many have predicted, is achievable using commercially-available, state-of-the-art EHRs.

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In the United States, approximately 4,000 pregnancies each year are affected by the two most common birth defects, spina bifida and anencephaly. Studies have shown that exposure to environmental chemicals before and after conception may adversely affect reproduction by inducing cell death or dysfunction, which leads to infertility, fetal loss, lowered weight at birth, or birth anomalies in the offspring. The objective of the study was to evaluate the relationship between Neural Tube Defect births and residence at conception in proximity to hazardous waste sites in the Texas-Mexico border region between 1993 and 2000. ^ The study design was a nested matched case-control and utilized secondary data from a project, “The role of chemical and biological factors in the etiology of neural tube birth defects births along the Texas-Mexico Border” (Irina Cech, Principal Investigator). Geographic Information Systems (GIS) database methods were used to compare Neural Tube Defects cases to controls on status of conception residence occurring within a one-mile radius from hazardous waste sites, as compared to conception residence further away. Information on the exposures was obtained from the OnTarget Database and Environment Protection Agency website. Conditional logistic regression was used for the matched case-control study to investigate the relationship between an outcome of being a case or a control and proximity to hazardous waste sites. ^ The result of the study showed a 36 percent non-significant increased risk of having an NTD birth associated with maternal proximity to abandoned hazardous waste sites (95% CI = 0.62–3.02). In addition, there was a 24% non-significant elevated risk of having an NTD birth when living in proximity to air pollutant sites than when living further away (95% CI = 0.67–2.32). Although this study did not find statistically significant associations, it will expand on the existing knowledge of the relationship between NTD and proximity to hazardous waste sites. ^

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Southeast Texas, including Houston, has a large presence of industrial facilities and has been documented to have poorer air quality and significantly higher cancer rates than the remainder of Texas. Given citizens’ concerns in this 4th largest city in the U.S., Mayor Bill White recently partnered with the UT School of Public Health to determine methods to evaluate the health risks of hazardous air pollutants (HAPs). Sexton et al. (2007) published a report that strongly encouraged analytic studies linking these pollutants with health outcomes. In response, we set out to complete the following aims: 1. determine the optimal exposure assessment strategy to assess the association between childhood cancer rates and increased ambient levels of benzene and 1,3-butadiene (in an ecologic setting) and 2. evaluate whether census tracts with the highest levels of benzene or 1,3-butadiene have higher incidence of childhood lymphohematopoietic cancer compared with census tracts with the lowest levels of benzene or 1,3-butadiene, using Poisson regression. The first aim was achieved by evaluating the usefulness of four data sources: geographic information systems (GIS) to identify proximity to point sources of industrial air pollution, industrial emission data from the U.S. EPA’s Toxic Release Inventory (TRI), routine monitoring data from the U.S. EPA Air Quality System (AQS) from 1999-2000 and modeled ambient air levels from the U.S. EPA’s 1999 National Air Toxic Assessment Project (NATA) ASPEN model. Further, once these four data sources were evaluated, we narrowed them down to two: the routine monitoring data from the AQS for the years 1998-2000 and the 1999 U.S. EPA NATA ASPEN modeled data. We applied kriging (spatial interpolation) methodology to the monitoring data and compared the kriged values to the ASPEN modeled data. Our results indicated poor agreement between the two methods. Relative to the U.S. EPA ASPEN modeled estimates, relying on kriging to classify census tracts into exposure groups would have caused a great deal of misclassification. To address the second aim, we additionally obtained childhood lymphohematopoietic cancer data for 1995-2004 from the Texas Cancer Registry. The U.S. EPA ASPEN modeled data were used to estimate ambient levels of benzene and 1,3-butadiene in separate Poisson regression analyses. All data were analyzed at the census tract level. We found that census tracts with the highest benzene levels had elevated rates of all leukemia (rate ratio (RR) = 1.37; 95% confidence interval (CI), 1.05-1.78). Among census tracts with the highest 1,3-butadiene levels, we observed RRs of 1.40 (95% CI, 1.07-1.81) for all leukemia. We detected no associations between benzene or 1,3-butadiene levels and childhood lymphoma incidence. This study is the first to examine this association in Harris and surrounding counties in Texas and is among the first to correlate monitored levels of HAPs with childhood lymphohematopoietic cancer incidence, evaluating several analytic methods in an effort to determine the most appropriate approach to test this association. Despite recognized weakness of ecologic analyses, our analysis suggests an association between childhood leukemia and hazardous air pollution.^

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The National Health Planning and Resources Development Act of 1974 (Public Law 93-641) requires that health systems agencies (HSAs) plan for their health service areas by the use of existing data to the maximum extent practicable. Health planning is based on the identificaton of health needs; however, HSAs are, at present, identifying health needs in their service areas in some approximate terms. This lack of specificity has greatly reduced the effectiveness of health planning. The intent of this study is, therefore, to explore the feasibility of predicting community levels of hospitalized morbidity by diagnosis by the use of existing data so as to allow health planners to plan for the services associated with specific diagnoses.^ The specific objectives of this study are (a) to obtain by means of multiple regression analysis a prediction equation for hospital admission by diagnosis, i.e., select the variables that are related to demand for hospital admissions; (b) to examine how pertinent the variables selected are; and (c) to see if each equation obtained predicts well for health service areas.^ The existing data on hospital admissions by diagnosis are those collected from the National Hospital Discharge Surveys, and are available in a form aggregated to the nine census divisions. When the equations established with such data are applied to local health service areas for prediction, the application is subject to the criticism of the theory of ecological fallacy. Since HSAs have to rely on the availability of existing data, it is imperative to examine whether or not the theory of ecological fallacy holds true in this case.^ The results of the study show that the equations established are highly significant and the independent variables in the equations explain the variation in the demand for hospital admission well. The predictability of these equations is good when they are applied to areas at the same ecological level but become poor, predominantly due to ecological fallacy, when they are applied to health service areas.^ It is concluded that HSAs can not predict hospital admissions by diagnosis without primary data collection as discouraged by Public Law 93-641. ^