Relevant bibliographies by topics / Health data warehousing evaluation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Health data warehousing evaluation'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Health data warehousing evaluation"

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Miller, Steven D., Phillip Stablein, Jay Syed, Valerie Smothers, Emily Marx, Peter Greene, Harold Lehmann, and Paul G. Nagy. "Evaluation of a Training Program to Improve Organizational Capacity for Health Systems Analytics." Applied Clinical Informatics 10, no. 04 (August 2019): 634–42. http://dx.doi.org/10.1055/s-0039-1694965.

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Objective The Leadership in Analytics and Data Science (LEADS) course was evaluated for effectiveness. LEADS was a 6-month program for working biomedical and health informatics (BMHI) professionals designed to improve analytics skills, knowledge of enterprise applications, data stewardship, and to foster an analytics community of practice through lectures, hands-on skill building workshops, networking events, and small group projects. Methods The effectiveness of the LEADS course was evaluated using the Kirkpatrick Model by assessing pre- and postcourse knowledge, analytics capabilities, goals, practice, class lecture reaction, and change in the size of participant professional networks. Differences in pre- and postcourse responses were analyzed with a Wilcoxon signed rank test to determine significance, and effect sizes were computed using a z-statistic. Results Twenty-nine students completed the course with 96% of respondents reporting that they were “very” or “extremely” likely to recommend the course. Participants reported improvement in several analytics capabilities including Epic data warehousing (p = 0.017), institutional review board policy (p = 0.005), and data stewardship (p = 0.007). Changes in practice patterns mirrored those in self-reported capability. On average, the participant professional network doubled. Conclusion LEADS was the first course targeted to working BMHI professional at a large academic medical center to have a formal effectiveness evaluation be published in the literature. The course achieved the goals of expansion of BMHI knowledge, skills, and professional networks. The LEADS course provides a template for continuing education of working BMHI professionals.
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Mahoney, Terrence. "Data warehousing and CI: An evaluation." Competitive Intelligence Review 9, no. 1 (January 1998): 38–43. http://dx.doi.org/10.1002/(sici)1520-6386(199801/03)9:1<38::aid-cir7>3.0.co;2-2.

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Spengler, Helmut, Claudia Lang, Tanmaya Mahapatra, Ingrid Gatz, Klaus A. Kuhn, and Fabian Prasser. "Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation." JMIR Medical Informatics 8, no. 7 (July 21, 2020): e15918. http://dx.doi.org/10.2196/15918.

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Background Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis. Objective Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges. Methods We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work. Results The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements. Conclusions Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation.
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Nicholson, Scott, Lynn Silipigni Connaway, and Bob Molyneux. "Using context to improve data-based library evaluation through data warehousing, data mining and visualization." Proceedings of the American Society for Information Science and Technology 43, no. 1 (October 10, 2007): 1–6. http://dx.doi.org/10.1002/meet.1450430164.

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Muntjir, Mohd. "Novice Evaluation and Comparative Survey on Database Management System, Data Warehousing and Data Mining." International Journal of Computer Applications 136, no. 10 (February 17, 2016): 39–45. http://dx.doi.org/10.5120/ijca2016908613.

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Berrar, Daniel, Frederic Stahl, Candida Silva, J. Rui Rodrigues, Rui M. M. Brito, and Werner Dubitzky. "Towards Data Warehousing and Mining of Protein Unfolding Simulation Data." Journal of Clinical Monitoring and Computing 19, no. 4-5 (October 2005): 307–17. http://dx.doi.org/10.1007/s10877-005-0676-z.

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Mattei, Michael D., and Stephen Hellebusch. "Simplified Data Analytics for the Accurate Evaluation of a New Venture's Market Potential." New England Journal of Entrepreneurship 9, no. 2 (March 1, 2006): 9–17. http://dx.doi.org/10.1108/neje-09-02-2006-b001.

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This article examines the creation of an accurate market projection designed with easy-to-use, cost-effective data analytic techniques. Many of the techniques explored are derived from the subdisciplines of decision support and data warehousing found in the information technology arena. Two significant contributions are presented: a simple mathematical technique that eliminates the need for heuristics, and the simplification of the process to the point where no computer or sophisticated statistical analysis is needed.
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Blatt, Robin J. R. "Banking Biological Collections: Data Warehousing, Data Mining, and Data Dilemmas in Genomics and Global Health Policy." Public Health Genomics 3, no. 4 (2000): 204–11. http://dx.doi.org/10.1159/000051140.

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Jaramillo, Alejandro. "Data Warehousing Solutions and Internet Initiatives in the Disease Management Era." Disease Management and Health Outcomes 9, no. 1 (2001): 1–9. http://dx.doi.org/10.2165/00115677-200109010-00001.

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Cifci, Mehmet Akif, and Sadiq Hussain. "Data Mining Usage and Applications in Health Services." JOIV : International Journal on Informatics Visualization 2, no. 4 (June 27, 2018): 225. http://dx.doi.org/10.30630/joiv.2.4.148.

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Data Mining (DM), used to extract large amounts of hidden, valuable, useful information in large quantities and to provide strategic decision support, has created a new perspective on the use of health data. It has become a rapidly growing method of responding to problematic areas of data in large quantities in almost all sections. Although in health services it seems to be slow, a major leap has come to the scene. The aim of this study is to provide a new perspective on decision-making processes by creating an infrastructure for the health data and to provide examples for healthcare workers in the healthcare industry using DM techniques. Forasmuch as, the conceptual framework of data discovery in databases, Data Warehousing, DM, Business Intelligence (BI) has been given. DM applications and usages are given as examples of priority issues and problem areas in the health sector.
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Dissertations / Theses on the topic "Health data warehousing evaluation"

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Otine, Charles. "Participatory approach to data warehousing in health care : UGANDA’S Perspective." Licentiate thesis, Karlskrona : Blekinge Institute of Technology, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00491.

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This licentiate thesis presents the use of participatory approach to developing a data warehouse for data mining in health care. Uganda is one of the countries that faced the largest brunt of the HIV/AIDS epidemic at its inception in the early 1980s with reports of close to a million deaths. Government and nongovernmental interventions over the years saw massive reductions in HIV prevalence rates over the years. This reduction in HIV prevalence rates led to great praises by the international community and a call for other countries to model Uganda’s approach to battling the epidemic. In the last decade the reduction in HIV prevalence rates have stagnated and in some cases increased. This has lead to a call for reexamination of the HIV/AIDS fight with an emphasis on collective efforts of all approaches. One of these collective efforts is the introduction of antiretroviral therapy (ART) for those already infected with the virus. Antiretroviral therapy has numerous challenges in Uganda not least of which is the cost of the therapy especially on a developing country with limited resources. It is estimated that of the close to 1 million infected in Uganda only 300,000 are on antiretroviral therapy (UNAIDS, 2009). Additional challenges of the therapy includes following through a treatment regimen that is prescribed. Given the costs of the therapy and the limited number of people able to access the therapy it is imperative that this effort be as effective as possible. This research hinges on using data mining techniques with monitoring HIV patient’s therapy, most specifically their adherence to ART medication. This is crucial given that failure to adhere to therapy means treatment failure, virus mutation and huge losses in terms of costs incurred in administering the therapy to the patients. A system was developed to monitor patient adherence to therapy, by using a participatory approach of gathering system specification and testing to ensure acceptance of the system by the stakeholders. Due to the cost implications of over the shelf software the development of the system was implemented using open source software with limited license costs. These can be implemented in resource constrained settings in Uganda and elsewhere to assist in monitoring patients in HIV therapy. A algorithm that is used to analyze the patient data warehouses for information on and quickly assists therapists in identifying potential risks such as non-adherence and treatment failure. Open source dimensional modeling tools power architect and DB designer were used to model the data warehouse using open source MYSQL database. The thesis is organized in three parts with the first part presenting the background information, the problem, justification, objectives of the research and a justification for the use of participatory methodology. The second part presents the papers, on which this research is based and the final part contains the summary discussions, conclusions and areas for future research. The research is sponsored by SIDA under the collaboration between Makerere University and Blekinge Institute of Technology (BTH) in Sweden.
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Khalid, Shehla. "Towards Data Governance for International Dementia Care Mapping (DCM). A Study Proposing DCM Data Management through a Data Warehousing Approach." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5226.

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Information Technology (IT) plays a vital role in improving health care systems by enhancing the quality, efficiency, safety, security, collaboration and informing decision making. Dementia, a decline in mental ability which affects memory, concentration and perception, is a key issue in health and social care, given the current context of an aging population. The quality of dementia care is noted as an international area of concern. Dementia Care Mapping (DCM) is a systematic observational framework for assessing and improving dementia care quality. DCM has been used as both a research and practice development tool internationally. However, despite the success of DCM and the annual generation of a huge amount of data on dementia care quality, it lacks a governance framework, based on modern IT solutions for data management, such a framework would provide the organisations using DCM a systematic way of storing, retrieving and comparing data over time, to monitor progress or trends in care quality. Data Governance (DG) refers to the implications of policies and accountabilities to data management in an organisation. The data management procedure includes availability, usability, quality, integrity, and security of the organisation data according to their users and requirements. This novel multidisciplinary study proposes a comprehensive solution for governing the DCM data by introducing a data management framework based on a data warehousing approach. Original contributions have been made through the design and development of a data management framework, describing the DCM international database design and DCM data warehouse architecture. These data repositories will provide the acquisition and storage solutions for DCM data. The designed DCM data warehouse facilitates various analytical applications to be applied for multidimensional analysis. Different queries are applied to demonstrate the DCM data warehouse functionality. A case study is also presented to explain the clustering technique applied to the DCM data. The performance of the DCM data governance framework is demonstrated in this case study related to data clustering results. Results are encouraging and open up discussion for further analysis.
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Chen, Qian. "Data blending in health care : Evaluation of data blending." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-202559.

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This report is aimed at those who are interested in data analysis and data blending. Decision making is crucial for an organization to succeed in today’s market. Data analysis is an important support activity in decision making and is applied in many industries, for example healthcare. For many years data analysts have worked on structured data in small volumes, with traditional methods such as spreadsheet. As new data sources emerged, such as social media, data is generated in higher volume, velocity and variety [1]. The traditional methods data analysts apply are no longer capable of handling this situation. Hence scientists and engineers have developed a new technology called data blending. Data blending is the process of merging, sorting, joining and combining all the useful data into a functional dataset [2]. Some of the well-known data blending platforms include Datawatch, Microsoft Power Query for Excel, IBM DataWorks and Alteryx [3]. Synergus AB is a consulting company engaged in health economics, market access and Health Technology Assessment (HTA) [4]. The company does analysis for their clients. Unfortunately the way they work is not efficient. New tools and methods need to be applied in the company. The company has decided to apply data blending in their daily work. My task in this project was to build datasets for analysis and create workflows for future use with a data blending platform. For my interest, I did a research on data blending to understand how this new technology works. During the project I have worked with four data sources. These were Microsoft Excel worksheet, CSV file, MS Access database and JSON file. I built datasets the company needs. I also preceded a case study on data blending process. I focused on the three steps of data handling, namely input, process and output. After the project, I reached a conclusion that data blending offers better performance and functionality. It is easy to learn and use, too.
Denna rapport vänder sig till de som är intresserad av data analys och datahantering. Belsut fattande är avgörande för en organisation att lyckas i dagens marknad. Data analys är en viktig stöd inom beslutfattande och tillämpas i många industrier, till exempel hälsovård. I många år har data analyster arbetat med strukturerad data i små volymer, med traditionella arbetsmetoder såsom kalkyblad. Med nya data källor uppstått, såsom sociala media, data är genererad i högre volym, högre hastighet och högre variation. De traditionella metoder data analyster använder är inte längre kapabla av att hantera denna situation. Därför har vetenskapsmän och ingenjörer utvecklat ett ny teknologi kallad datahantering. Datahantering är en process för att sammanfoga, sortera och kombinera all värdeful data till en funktionell dataset. Några av de välkända datahanteringsplatformer inkluderar Datawatch, Microsoft Power Query for Excel, IBM DataWorks and Alteryx. Synergus AB är ett konsultföretag engagerad inom hälsoekonomi, marknad tillträde, och Health Technology Assessment (HTA). Företaget gör analys för deras kunder. Tyvärr är de arbetsmetoder inom företaget inte effektiv. Nya verktyg och metoder måste tillämpas inom företaget. Synergus AB har beslutat att tillämpa datahantering i deras dagliga arbete. Mitt uppdrag i detta projekt var att bygga dataset för analys och skapa arbetsflöde för framtida användning med en datahanteringsplatform. För mitt eget intresse, jag utförde en studie av datahantering för att förstå hur denna nya teknologi fungerar. Under projektet har jag arbetat med fyra data källor. De var Microsft Excel kalkylblad, CSV fil, MS Access databas och JSON fil. Jag byggde dataset företaget behöver. Jag också utförde ett fall studie om datahanteringsprocess. Jag fokuserade mig på de tre steg inom datahantering, nämligen inmatning, bearbetning och utmatning. Efter projektet kom jag till en slutsats att datahantering erjuder bättre prestanda och funktionelitet. Det är också lätt att lära sig och använda.
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Kairouz, Joseph. "Patient data management system medical knowledge-base evaluation." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=24060.

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The purpose of this thesis is to evaluate the medical data management expert system at the Pediatric Intensive Care Unit of the Montreal Children's Hospital. The objective of this study is to provide a systematic method to evaluate and, progressively improve the knowledge embedded in the medical expert system.
Following a literature survey on evaluation techniques and architecture of existing expert systems, an overview of the Patient Data Management System hardware and software components is presented. The design of the Expert Monitoring System is elaborated. Following its installation in the intensive Care Unit, the performance of the Expert Monitoring System is evaluated, operating on real vital sign data and corrections were formulated. A progressive evaluation technique, new methodology for evaluating an expert system knowledge-base is proposed for subsequent corrections and evaluations of the Expert Monitoring System.
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Vela, Sandra A. "Canadian life and health insurance productivity evaluation using data envelopment analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0023/MQ50377.pdf.

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Fraker, Shannon E. "Evaluation of Scan Methods Used in the Monitoring of Public Health Surveillance Data." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/29511.

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With the recent increase in the threat of biological terrorism as well as the continual risk of other diseases, the research in public health surveillance and disease monitoring has grown tremendously. There is an abundance of data available in all sorts of forms. Hospitals, federal and local governments, and industries are all collecting data and developing new methods to be used in the detection of anomalies. Many of these methods are developed, applied to a real data set, and incorporated into software. This research, however, takes a different view of the evaluation of these methods. We feel that there needs to be solid statistical evaluation of proposed methods no matter the intended area of application. Using proof-by-example does not seem reasonable as the sole evaluation criteria especially concerning methods that have the potential to have a great impact in our lives. For this reason, this research focuses on determining the properties of some of the most common anomaly detection methods. A distinction is made between metrics used for retrospective historical monitoring and those used for prospective on-going monitoring with the focus on the latter situation. Metrics such as the recurrence interval and time-to-signal measures are therefore the most applicable. These metrics, in conjunction with control charts such as exponentially weighted moving average (EWMA) charts and cumulative sum (CUSUM) charts, are examined. Two new time-to-signal measures, the average time-between-signal events and the average signal event length, are introduced to better compare the recurrence interval with the time-to-signal properties of surveillance schemes. The relationship commonly thought to exist between the recurrence interval and the average time to signal is shown to not exist once autocorrelation is present in the statistics used for monitoring. This means that closer consideration needs to be paid to the selection of which of these metrics to report. The properties of a commonly applied scan method are also studied carefully in the strictly temporal setting. The counts of incidences are assumed to occur independently over time and follow a Poisson distribution. Simulations are used to evaluate the method under changes in various parameters. In addition, there are two methods proposed in the literature for the calculation of the p-value, an adjustment based on the tests for previous time periods and the use of the recurrence interval with no adjustment for previous tests. The difference in these two methods is also considered. The quickness of the scan method in detecting an increase in the incidence rate as well as the number of false alarm events that occur and how long the method signals after the increase threat has passed are all of interest. These estimates from the scan method are compared to other attribute monitoring methods, mainly the Poisson CUSUM chart. It is shown that the Poisson CUSUM chart is typically faster in the detection of the increased incidence rate.
Ph. D.
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Du, Hank C. T. "An evaluation of the contribution of pharmacy sales data for purposes of public health." Thesis, Cardiff University, 2013. http://orca.cf.ac.uk/58915/.

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The contribution of over-the-counter (OTC) medicines sales data from pharmacies for public health (PH) has previously attracted interest in the UK. In this study, data for several OTC medicines were utilised to explore their contribution to (a) understand the impact of medicine reclassification or increased regulation on supply and (b) the surveillance of infectious diseases in the community in Wales. Following the reclassification of ophthalmic chloramphenicol (June 2005) an increase in primary care supply (OTC + prescription) of 54% (47,026 units) in eye drops and 29% (15,657 units) in eye ointment were observed (2004 to 2010). Despite this increase the items of eye drops prescribed were similar 12 months before and five years after the reclassification. The impact of regulatory changes concerning the non-prescription sale of opioid-containing analgesics was studied. In the 12 months following September 2009 legislative changes there was a significant fall in sales of codeine- and dihydrocodeinecontaining solid oral dosage forms (p<0.05). Similarly, following the pack size restriction of non-prescription pseudoephedrine and ephedrine products (April 2008), significant (p<0.05) year-on-year reductions in the total weight of pseudoephedrine sold were observed. Sales of non-prescription ophthalmic chloramphenicol were monitored on a small area basis in two areas with known outbreaks of infective conjunctivitis. In both areas sales data did not demonstrate the required sensitivity. When monitoring seasonal influenza, significant positive correlations were observed between cough/cold/flu medicines sales and indicators of influenza activity in Wales. In alignment with the professional standards for PH practice for pharmacy produced by the Royal Pharmaceutical Society, the work undertaken demonstrated a number of potential uses of medicines sales data for PH. Routine data collection, particularly if captured at time/point of sale, would further enhance its usefulness in detecting and tracking PH incidents.
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Towers, Isabel Margaret Falcon. "The valuation of health outcomes data from clinical trials for use in economic evaluation." Thesis, University of Sheffield, 2005. http://etheses.whiterose.ac.uk/6075/.

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Kelman, Christopher William, and christopher kelman@cmis csiro au. "Monitoring Health Care Using National Administrative Data Collections." The Australian National University. National Centre for Epidemiology and Population Health, 2001. http://thesis.anu.edu.au./public/adt-ANU20020620.151547.

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With the inevitable adoption of information technology into all areas of human pursuit, the potential benefits for health care should not be overlooked. In Australia, details of most health care encounters are currently recorded for administrative purposes. This results in an impressive electronic data-bank that could provide a national resource for health service evaluation. ¶ Evaluation of health services has become increasingly important to provide indicators of the benefits, risks and cost-effectiveness of treatments. However, if administrative data are to be used for this purpose, several questions must first be addressed: Are the current data collections accessible? What outcome measures can be derived from these data? Can privacy issues be managed? Could the quality of the data be improved? Is the existing infrastructure adequate to supply data for evaluation purposes? Could the existing system provide a basis for the development of an integrated health information system? ¶ The aims of the project were: · To examine the potential for using administrative data to generate outcome measures and surveillance indicators. · To investigate the logistics of gaining access to these data for the purpose of research. This to be achieved within the current ethical, political and financial framework. · To compare the Australian health-service data system with the current international state-of-the-art. · To develop suggestions for expansion of the present system as part of an integrated health record and information system. This system to manage patient records and provide data for quality management, treatment surveillance and cost-effectiveness evaluation as a routine activity. ¶ The thesis is presented in two parts. In the first part, a historical cohort study is described that involved patients with implantable medical devices. The potential to evaluate outcomes was investigated using all national health-service information currently available in electronic form. Record linkage techniques were used to combine and augment the existing data collections. Australia’s national health databases are to varying degrees, amenable to such linkage and cover doctor visits, pharmaceuticals, hospital admissions and deaths. The study focused on medical devices as an illustrative case but the results are applicable to the routine assessment of all medical and surgical interventions. ¶ For the Australian ‘Medical Devices study’, the records of 5,316 patients who had medical device implants in 1993-94 were selected from the archives of a major private health insurer. Five groups of medical implants were studied: heart valves, pacemakers, hips, vascular grafts and intra-optic lenses. Outcomes for these patients, including death, re-operation and health service utilisation, were compared and analysed. ¶ A comparison study was performed using data from the Manitoba Health database in Winnipeg, Canada. Manitoba provides a very similar demographic group to that found in Australia and is an example of a prototype integrated-health-information system. One of the principal advantages for research is that personally identified data about medical and hospital services are collected for all patients. Selection bias is eliminated because individual consent is not required for this type of research and all selected patients could be included in the study. ¶ The two studies revealed many barriers to the use of administrative data for health outcomes research. Service event data for the Australian cohort could be collected but only after long delays and hospital morbidity data were not available for the entire cohort. In contrast to the situation in Australia, the Manitoba data were both accessible and complete, but were lacking in detail in some areas. ¶ Analysis of the collected data demonstrated that without the addition of clinical data only general indications of trends could be deduced. However, with minimal supplementary clinical data, it was possible to examine differences in performance between brands of medical devices thus indicating one of the uses for this type of data collection. ¶ In the second part of the thesis, conclusions are presented about the potential uses and limitations of the existing system and its use as a basis for the development of a national Integrated Health Record and Information System (IHRIS). The need for the establishment of a systemic quality management system for health care is discussed. ¶ The study shows that linked administrative data can provide information about health outcomes which is not readily available from other sources. If expanded and integrated, the system that is currently used to collect and manage administrative data, could provide the basis for a national health information system. This system would provide many benefits for health care. Benefits would include the monitoring, surveillance and cost-effectiveness analysis of new and existing treatments involving medical devices, drugs and surgical procedures. An integrated health information system could thus provide for both clinical and administrative needs, while in addition providing data for research. ¶ Unfortunately, in Australia, the use of administrative data for this purpose is not currently feasible. The principal barrier is the existence of a culture within the Australian health care system which is not supportive of research and is deficient in quality and safety measures. ¶ Recent initiatives by both the Commonwealth and state governments have supported the introduction of measures to improve quality and safety in health care. It is argued here that an Integrated Health Record and Information System (IHRIS) would provide an essential component of any such scheme. The results of this study have important policy implications for health care management in both the administrative and clinical domains.
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Olupot-Olupot, Peter. "Evaluation of Antiretroviral Therapy Information System In Mbale Regional Referral Hospital, Uganda." Thesis, University of the Western Cape, 2008. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_7320_1272589584.

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HIV/AIDS is the largest and most serious global epidemic in the recent times. To date, the epidemic has affected approximately 40 million people (range 33 &ndash
46 million) of whom 67%, that is, an estimated 27 million people are in the Sub Saharan Africa. The Sub Saharan Africa is also reported to have the highest regional prevalence of 7.2% compared to an average of 2% in other regions. A medical cure for HIV/AIDS remains elusive but use of antiretroviral therapy (ART) has resulted in improvement of quality and quantity of life as evidenced by the reduction of mortality and morbidity associated with the infection, hence longer and good quality life for HIV/AIDS patients on ART.

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Books on the topic "Health data warehousing evaluation"

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Oke, Philip. Data Warehousing: A critical evaluation. London: University ofEast London, 1997.

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R, Steckler Allan, and Goodman Robert M, eds. Measurement and evaluation of health education. 3rd ed. Springfield, Ill: C.C. Thomas Publisher, 1995.

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Measurement and evaluation of health education. Springfield, Ill., U.S.A: Thomas, 1986.

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Measurement and evaluation of health education. 2nd ed. Springfield, Ill., U.S.A: C.C. Thomas, 1989.

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Huston, D. Structural sensing, health monitoring, and performance evaluation. Boca Raton, FL: CRC Press, 2011.

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Huston, D. Structural sensing, health monitoring, and performance evaluation. Boca Raton: Taylor & Francis, 2010.

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Vela, Sandra A. Canadian life and health insurance productivity evaluation using data envelopment analysis. Ottawa: National Library of Canada, 2000.

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Structural sensing, health monitoring, and performance evaluation. Boca Raton: Taylor & Francis, 2010.

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Oliver, Guy W. 1988 evaluation of Alaska's mental health management information system. [Juneau, Alaska]: Alaska Mental Health Board, 1989.

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der, Gaag J. van, Van Rooy Gert, Muñoz Martín Juan, and Schier Christa, eds. Baseline data findings for the Okambilimbili Health Insurance Evaluation Project in Namibia. [Windhoek]: Multi-disciplinary Research Centre, University of Namibia, 2007.

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Book chapters on the topic "Health data warehousing evaluation"

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Ben-Eliyahu-Zohary, Rachel, and Ehud Gudes. "Meta-queries - Computation and Evaluation." In Data Warehousing and Knowledge Discovery, 265–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44466-1_26.

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Bertino, Elisa, and Igor Nai Fovino. "Information Driven Evaluation of Data Hiding Algorithms." In Data Warehousing and Knowledge Discovery, 418–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11546849_41.

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Ammendola, Christian, Michael H. Böhlen, and Johann Gamper. "Efficient Evaluation of Ad-Hoc Range Aggregates." In Data Warehousing and Knowledge Discovery, 46–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40131-2_5.

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Henle, Theresa, Gregory J. Matthews, and Ofer Harel. "Data Confidentiality." In Health Services Evaluation, 717–31. New York, NY: Springer US, 2019. http://dx.doi.org/10.1007/978-1-4939-8715-3_28.

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Mullner, Ross M. "Health Services Data: Typology of Health Care Data." In Health Services Evaluation, 77–108. New York, NY: Springer US, 2019. http://dx.doi.org/10.1007/978-1-4939-8715-3_6.

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Bellatreche, Ladjel, Kamalakar Karlapalem, and Qing Li. "Evaluation of Materialized View Indexing in Data Warehousing Environments." In Data Warehousing and Knowledge Discovery, 57–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44466-1_6.

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Feng, Ling, Qing Li, and Allan Wong. "Mining Inter-Transactional Association Rules: Generalization and Empirical Evaluation." In Data Warehousing and Knowledge Discovery, 31–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44801-2_4.

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de Carvalho, Veronica Oliveira, Fabiano Fernandes dos Santos, and Solange Oliveira Rezende. "Metrics to Support the Evaluation of Association Rule Clustering." In Data Warehousing and Knowledge Discovery, 248–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40131-2_21.

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Lin, Weiqiang, Mehmet A. Orgun, and Graham J. Williams. "Mining Temporal Patterns from Health Care Data*." In Data Warehousing and Knowledge Discovery, 222–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46145-0_22.

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Mishra, P., and S. Chakravarthy. "Performance Evaluation of SQL-OR Variants for Association Rule Mining." In Data Warehousing and Knowledge Discovery, 288–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45228-7_29.

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Conference papers on the topic "Health data warehousing evaluation"

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Ewen, Edward F., Carl E. Medsker, and Laura E. Dusterhoft. "Data warehousing in an integrated health system." In the 1st ACM international workshop. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/294260.294271.

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Liyanage, Liwan H., and Sushan H. Liyanage. "Data Integration and Data mining Framework to Discover Health Impacts of Climate Change." In Annual International Academic Conference on Business Intelligence and Data Warehousing. Global Science and Technology Forum, 2010. http://dx.doi.org/10.5176/978-981-08-6308-1_d-002.

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Lean, Stephen, Hans W. Guesgen, Inga Hunter, and Kudakwashe Dube. "Computational Confidence for Decision Making in Health." In Annual International Academic Conference on Business Intelligence and Data Warehousing. Global Science and Technology Forum, 2010. http://dx.doi.org/10.5176/978-981-08-6308-1_d-033.

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Wong, Peter Wai Yee, Ric Hendrickson, Haider Rizvi, and Steve Pratt. "Performance evaluation of linux file systems for data warehousing workloads." In the 1st international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1146847.1146890.

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Nimmagadda, Shastri L., Sashi K. Nimmagadda, and Heinz Dreher. "Multidimensional data warehousing & mining of diabetes & food-domain ontologies for e-Health." In 2011 9th IEEE International Conference on Industrial Informatics (INDIN). IEEE, 2011. http://dx.doi.org/10.1109/indin.2011.6034973.

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Chala, Sisay, Fazel Ansari, and Madjid Fathi. "A data warehousing system for knowledge-based structural health monitoring of wind power plant." In 2016 IEEE International Conference on Electro Information Technology (EIT). IEEE, 2016. http://dx.doi.org/10.1109/eit.2016.7535307.

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Harris, Romona M. "Data Warehousing and Decision Support System Effectiveness Demonstrated in Service Recovery During COVID19 Health Pandemic." In 2020 14th International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2020. http://dx.doi.org/10.1109/icosst51357.2020.9333019.

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Turhan, Sultan, and Ozgun Pinarer. "QUERY PERFORMANCE EVALUATION OVER HEALTH DATA." In International Conference on e-Health 2019. IADIS Press, 2019. http://dx.doi.org/10.33965/eh2019_201910l013.

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JANAPATI, VISHNUVARDHAN, HOWARD CHUNG, FRANKLIN LI, AMRITA KUMAR, and SAMUEL HUANG. "Evaluation of Long-term Flight Data from On-board SMART Layers." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/335.

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Bangsgaard, Torben B., Henrik Gjelstrup, Andrew Scullion, and Paul Faulkner. "Structural Health Monitoring System for The Queensferry Crossing." In IABSE Conference, Copenhagen 2018: Engineering the Past, to Meet the Needs of the Future. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2018. http://dx.doi.org/10.2749/copenhagen.2018.431.

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The Structural Health Monitoring System (SHMS) for the new Queensferry Crossing cable stayed bridge, Scotland include more than 1500 sensors combined to yield a world leading SHMS for data driven asset management making use of the latests technologies in data processesing and data warehousing.
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Reports on the topic "Health data warehousing evaluation"

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Abel, Keith H., Ted W. Bowyer, James C. Hayes, Tom R. Heimbigner, Mark E. Panisko, Justin I. McIntyre, and Robert C. Thompson. Ideas and Concepts for Diagnosis of Performance and Evaluation of Data Reliability Based Upon ARSA State-of-Health (SOH) Data. Office of Scientific and Technical Information (OSTI), April 2000. http://dx.doi.org/10.2172/15001063.

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KH Abel, TW Bowyer, JC Hayes, TR Heimbigner, ME Panisko, JI McIntyre, and RC Thompson. Ideas and concepts for diagnosis of performance and evaluation of data reliability based upon ARSA state-of-health (SOH) data. Office of Scientific and Technical Information (OSTI), April 2000. http://dx.doi.org/10.2172/754183.

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Chan, Philemon C., Kevin H. Ho, Kit K. Kan, and James H. Stuhmiller. A Health Hazard Assessment for Blast Overpressure Exposures Subtitle - Evaluation of Impulse Noise Criteria Using Human Volunteer Data. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada394948.

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Treadwell, Jonathan R., James T. Reston, Benjamin Rouse, Joann Fontanarosa, Neha Patel, and Nikhil K. Mull. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Agency for Healthcare Research and Quality (AHRQ), March 2021. http://dx.doi.org/10.23970/ahrqepctb38.

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Background. Automated-entry consumer devices that collect and transmit patient-generated health data (PGHD) are being evaluated as potential tools to aid in the management of chronic diseases. The need exists to evaluate the evidence regarding consumer PGHD technologies, particularly for devices that have not gone through Food and Drug Administration evaluation. Purpose. To summarize the research related to automated-entry consumer health technologies that provide PGHD for the prevention or management of 11 chronic diseases. Methods. The project scope was determined through discussions with Key Informants. We searched MEDLINE and EMBASE (via EMBASE.com), In-Process MEDLINE and PubMed unique content (via PubMed.gov), and the Cochrane Database of Systematic Reviews for systematic reviews or controlled trials. We also searched ClinicalTrials.gov for ongoing studies. We assessed risk of bias and extracted data on health outcomes, surrogate outcomes, usability, sustainability, cost-effectiveness outcomes (quantifying the tradeoffs between health effects and cost), process outcomes, and other characteristics related to PGHD technologies. For isolated effects on health outcomes, we classified the results in one of four categories: (1) likely no effect, (2) unclear, (3) possible positive effect, or (4) likely positive effect. When we categorized the data as “unclear” based solely on health outcomes, we then examined and classified surrogate outcomes for that particular clinical condition. Findings. We identified 114 unique studies that met inclusion criteria. The largest number of studies addressed patients with hypertension (51 studies) and obesity (43 studies). Eighty-four trials used a single PGHD device, 23 used 2 PGHD devices, and the other 7 used 3 or more PGHD devices. Pedometers, blood pressure (BP) monitors, and scales were commonly used in the same studies. Overall, we found a “possible positive effect” of PGHD interventions on health outcomes for coronary artery disease, heart failure, and asthma. For obesity, we rated the health outcomes as unclear, and the surrogate outcomes (body mass index/weight) as likely no effect. For hypertension, we rated the health outcomes as unclear, and the surrogate outcomes (systolic BP/diastolic BP) as possible positive effect. For cardiac arrhythmias or conduction abnormalities we rated the health outcomes as unclear and the surrogate outcome (time to arrhythmia detection) as likely positive effect. The findings were “unclear” regarding PGHD interventions for diabetes prevention, sleep apnea, stroke, Parkinson’s disease, and chronic obstructive pulmonary disease. Most studies did not report harms related to PGHD interventions; the relatively few harms reported were minor and transient, with event rates usually comparable to harms in the control groups. Few studies reported cost-effectiveness analyses, and only for PGHD interventions for hypertension, coronary artery disease, and chronic obstructive pulmonary disease; the findings were variable across different chronic conditions and devices. Patient adherence to PGHD interventions was highly variable across studies, but patient acceptance/satisfaction and usability was generally fair to good. However, device engineers independently evaluated consumer wearable and handheld BP monitors and considered the user experience to be poor, while their assessment of smartphone-based electrocardiogram monitors found the user experience to be good. Student volunteers involved in device usability testing of the Weight Watchers Online app found it well-designed and relatively easy to use. Implications. Multiple randomized controlled trials (RCTs) have evaluated some PGHD technologies (e.g., pedometers, scales, BP monitors), particularly for obesity and hypertension, but health outcomes were generally underreported. We found evidence suggesting a possible positive effect of PGHD interventions on health outcomes for four chronic conditions. Lack of reporting of health outcomes and insufficient statistical power to assess these outcomes were the main reasons for “unclear” ratings. The majority of studies on PGHD technologies still focus on non-health-related outcomes. Future RCTs should focus on measurement of health outcomes. Furthermore, future RCTs should be designed to isolate the effect of the PGHD intervention from other components in a multicomponent intervention.
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Daniels, J. I., and D. W. Layton. Evaluation of military field-water quality: Volume 9, Data for assessing health risks in potential theaters of operation for US military forces: (Final report). Office of Scientific and Technical Information (OSTI), February 1988. http://dx.doi.org/10.2172/6841103.

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Smith, Gretchen C., John W. Coulston, and Barbara M. O'Connell. Ozone bioindicators and forest health: a guide to the evaluation, analysis, and interpretation of the ozone injury data in the Forest Inventory and Analysis Program. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station, 2008. http://dx.doi.org/10.2737/nrs-gtr-34.

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Lester, Paul B., P. D. Harms, Mitchel N. Herian, Dina V. Krasikova, and Sarah J. Beal. The Comprehensive Soldier Fitness Program Evaluation. Report 3: Longitudinal Analysis of the Impact of Master Resilience Training on Self-Reported Resilience and Psychological Health Data. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada553635.

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Wiecha, Jean L., and Mary K. Muth. Agreements Between Public Health Organizations and Food and Beverage Companies: Approaches to Improving Evaluation. RTI Press, January 2021. http://dx.doi.org/10.3768/rtipress.2021.op.0067.2101.

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Efforts in the United States and abroad to address the chronic disease epidemic have led to the emergence of voluntary industry agreements as a substitute for regulatory approaches to improve the healthfulness of foods and beverages. Because of the lack of access to data and limited budgets, evaluations of these agreements have often been limited to process evaluation with less focus on outcomes and impact. Increasing scientific scope and rigor in evaluating voluntary food and beverage industry agreements would improve potential public health benefits and understanding of the effects of these agreements. We describe how evaluators can provide formative, process, and outcome assessment and discuss challenges and opportunities for impact assessment. We explain how logic models, industry profiles, quasi-experimental designs, mixed-methods approaches, and third-party data can improve the effectiveness of agreement design and evaluation. These methods could result in more comprehensive and rigorous evaluation of voluntary industry agreements, thus providing data to bolster the public health impacts of future agreements. However, improved access to data and larger evaluation budgets will be needed to support improvements in evaluation.
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Leavy, Michelle B., Danielle Cooke, Sarah Hajjar, Erik Bikelman, Bailey Egan, Diana Clarke, Debbie Gibson, Barbara Casanova, and Richard Gliklich. Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Report on Registry Configuration. Agency for Healthcare Research and Quality (AHRQ), November 2020. http://dx.doi.org/10.23970/ahrqepcregistryoutcome.

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Background: Major depressive disorder is a common mental disorder. Many pressing questions regarding depression treatment and outcomes exist, and new, efficient research approaches are necessary to address them. The primary objective of this project is to demonstrate the feasibility and value of capturing the harmonized depression outcome measures in the clinical workflow and submitting these data to different registries. Secondary objectives include demonstrating the feasibility of using these data for patient-centered outcomes research and developing a toolkit to support registries interested in sharing data with external researchers. Methods: The harmonized outcome measures for depression were developed through a multi-stakeholder, consensus-based process supported by AHRQ. For this implementation effort, the PRIME Registry, sponsored by the American Board of Family Medicine, and PsychPRO, sponsored by the American Psychiatric Association, each recruited 10 pilot sites from existing registry sites, added the harmonized measures to the registry platform, and submitted the project for institutional review board review Results: The process of preparing each registry to calculate the harmonized measures produced three major findings. First, some clarifications were necessary to make the harmonized definitions operational. Second, some data necessary for the measures are not routinely captured in structured form (e.g., PHQ-9 item 9, adverse events, suicide ideation and behavior, and mortality data). Finally, capture of the PHQ-9 requires operational and technical modifications. The next phase of this project will focus collection of the baseline and follow-up PHQ-9s, as well as other supporting clinical documentation. In parallel to the data collection process, the project team will examine the feasibility of using natural language processing to extract information on PHQ-9 scores, adverse events, and suicidal behaviors from unstructured data. Conclusion: This pilot project represents the first practical implementation of the harmonized outcome measures for depression. Initial results indicate that it is feasible to calculate the measures within the two patient registries, although some challenges were encountered related to the harmonized definition specifications, the availability of the necessary data, and the clinical workflow for collecting the PHQ-9. The ongoing data collection period, combined with an evaluation of the utility of natural language processing for these measures, will produce more information about the practical challenges, value, and burden of using the harmonized measures in the primary care and mental health setting. These findings will be useful to inform future implementations of the harmonized depression outcome measures.
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Ripoll, Santiago, Jennifer Cole, Olivia Tulloch, Megan Schmidt-Sane, and Tabitha Hrynick. SSHAP: 6 Ways to Incorporate Social Context and Trust in Infodemic Management. Institute of Development Studies (IDS), January 2021. http://dx.doi.org/10.19088/sshap.2021.001.

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Information epidemiology or infodemiology is the study of infodemics - defined by the World Health Organization as an overabundance of information, some accurate and some not, that occurs during a pandemic or other significant event that may impact public health. Infodemic management is the practice of infodemiology and may sit within the risk communication and community engagement (RCCE) pillar of a public health response. However, it is relevant to all aspects of preparedness and response, including the development and evaluation of interventions. Social scientists have much to contribute to infodemic management as, while it must be data and evidence driven, it must also be built on a thorough understanding of affected communities in order to develop participatory approaches, reinforce local capacity and support local solutions.
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