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This document provides an overview of a hands-on workshop for understanding and using data from the National Ambulatory Medical Care Survey (NAMCS) and the National Hospital Ambulatory Medical Care Survey (NHAMCS). The workshop covers survey background and data uses, technical considerations, accessing the data online, using raw data files, and advanced topics. NAMCS and NHAMCS collect data on visits to office-based physicians and hospital outpatient/emergency departments, including patient characteristics, visit details, provider information, and mentions of up to six medications per visit.
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Mccaig Woodwell2
The document summarizes the National Ambulatory Medical Care Survey (NAMCS) and the National Hospital Ambulatory Medical Care Survey (NHAMCS), which collect data on outpatient visits through annual probability samples. Key points include:
1) NAMCS collects data from office-based physicians while NHAMCS collects data from emergency and outpatient departments.
2) The surveys were established in the 1970s and track health care practices, conditions, priorities, and Healthy People objectives.
3) Data is used by medical groups, government agencies, researchers and media to inform policy, study prescribing rates, and more.
The Role of Real-World Data in Clinical Development
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Safti net overview ahrq stakeholders mtg oct 2011
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FMS Cost & Frequency of Care
This study used electronic health records data to evaluate health care resource utilization in 64,038 patients diagnosed with fibromyalgia. The highest utilization was for medication orders and physician office visits, with patients receiving an average of 21 medication orders and 15 office visits in the year following diagnosis. Opioids were the most commonly prescribed medications, used by 44.3% of patients. Factors associated with increased resource use included being female, specific comorbid conditions, and use of opioids not recommended by fibromyalgia guidelines. The study aims to identify drivers of health care costs in fibromyalgia to help improve patient management and reduce utilization.
Electronic Medical Records: From Clinical Decision Support to Precision Medicine
Electronic Medical Records: From Clinical Decision Support to Precision MedicineKent State University
This document discusses the transition from traditional clinical decision support using electronic medical records to precision medicine. It provides examples of how Cleveland Clinic has used electronic medical records to create registries for conditions like chronic kidney disease, develop predictive models, and power algorithms for precision treatment recommendations. The document envisions precision medicine relying on vast amounts of molecular, genomic, and patient-reported data integrated into clinical decision support.Re-admit Historical using SAS Visual Analytics
- Hospital readmissions are costly and result in $15-20 billion in expenses annually in the US. Preventing avoidable readmissions can improve patient quality of life and reduce healthcare costs.
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- Reducing preventable readmissions requires improving care coordination, patient education, and post-discharge support to ensure patients understand their treatment plan and who to contact if issues arise. The CMS Hospital Readmission Reduction Program financially penalizes hospitals with excess readmissions for certain conditions like heart failure to incentivize lower
Patient-Reported Outcomes in Cancer Care - Zeena Nackerdien
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Clinical Research Informatics (CRI) Year-in-Review 2014
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This document discusses the debate between randomized clinical trials (RCTs) and observational studies using big data. While RCTs are better for minimizing bias, observational studies can include more patients and answer questions RCTs cannot. The document outlines several large cancer databases that can help learn from every patient, including SEER and NCDB registries. It describes how these databases are being enriched with additional data sources like EHRs, genomic data, and mobile devices. This evolving use of big data from numerous sources can improve outcomes by better understanding toxicity, costs, and quality of cancer care.
AUTOMATED DATABASES INTRO.docx
Post marketing studies of drug effects must then generally include at least 10,000 exposed persons in a cohort study, or enroll diseased patients from a population of equivalent size for a case–control study. A study of this size would be 95% certain of observing at least one case of any adverse effect that occurs with an incidence of 3 per 10 000 or greater (see Chapter 3). However, studies this large are expensive and difficult to perform. Yet, these studies often need to be conducted quickly, to address acute and serious regulatory, commercial, and/or public health crises. For all of these reasons, the past two decades have seen a growing use of computerized databases containing medical care data, so called “automated databases,” as potential data sources for pharmacoepidemiology studies.
Comparisonof Clinical Diagnoses versus Computerized Test Diagnoses Using the ...
The Diagnostic Paradigm from www.MarylandClinicalDiagnostics.com was able to help the former Dean of Los Angeles Chiropractic College detect medical diagnoses which he had overlooked, and he later confirmed.
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This document discusses the importance of real-world evidence in medicine. Real-world evidence provides information about therapeutic agents from clinical practice that complements data from randomized controlled trials. Sources of real-world evidence include electronic medical records, billing databases, product registries, and disease registries. Real-world evidence is useful for generalizing trial findings to broader patient populations, outcomes research, and safety surveillance outside of traditional clinical trials. However, real-world evidence studies also have limitations like potential data quality issues and difficulty controlling for biases compared to randomized trials.
Readmission of Diabetes Patients Report
This document provides a summary of a project to analyze factors related to readmission of diabetes patients using a dataset from 130 US hospitals. The team cleaned the data by removing attributes with high percentages of missing values, irrelevant attributes, and instances of deceased patients. They applied the SMOTE technique to address data imbalance, oversampling the minority readmission class by 200%. Three classifiers - J48 decision tree, Naive Bayes, and Bayes Net - were selected for experiments to predict patient readmission.
Multi criteria decision analysis for healthcare
Multi-criteria decision analysis (MCDA) is a process used to evaluate multiple criteria in healthcare decision making. Three key aspects of MCDA are summarized:
1. MCDA identifies criteria like clinical benefits, safety, costs, and other factors to evaluate healthcare interventions. Studies consider criteria like disease severity, population affected, clinical guidelines, and costs.
2. Interventions are measured and scored on each criterion using evidence and expert opinion. Scores are often on predefined scales.
3. Criteria weights reflecting importance are generated, commonly using analytic hierarchy process or defined scales. Weights involve multiple stakeholders.
Sensitivity analysis assesses uncertainty, though many studies do not report this. MCD
Electronic health records and machine learning
Electronic health records and machine learning can be used together to generate real-world evidence. Real-world data is collected from electronic health records in real clinical settings and can provide insights into a treatment's effectiveness and safety outside of clinical trials. Machine learning models can analyze structured and unstructured data in electronic health records to identify patterns and make predictions. This can help with tasks like medical diagnosis, which is challenging due to variations between individuals and potential for misdiagnosis. However, developing accurate machine learning models requires addressing issues like selecting representative training data and setting performance standards.
Health care special interest-i2b2
This document discusses using natural language processing (NLP) techniques to extract information from clinical notes. It describes past challenges that aimed to extract disease status and medication information, achieving 95-97% and 80% accuracy respectively. Successful approaches involved building clinical term dictionaries, context patterns related to diseases and medications, and domain knowledge to represent complex clinical facts and conditions. The goal is to support clinical data analytics and quality monitoring.
Using real-world evidence to investigate clinical research questions
Adoption of electronic health records to document extensive clinical information brings with it the opportunity to utilise that information to support clinical research, and ultimately to support clinical decision making. In this talk, I discuss both these opportunities and the challenges that we face when working with real-world clinical data, and introduce some of the strategies that we are adopting to make this data more usable, and to extract more value from it. I specifically discuss the use of natural language processing to transform clinical documentation into structured data for this purpose.
Analysis of Medication Possession Ratio for Improved Blood Pressure Control
Analysis of Medication Possession Ratio for Improved Blood Pressure ControlHealth Informatics New Zealand
Thusitha Mabotuwana
Department of Computer Science
University of Auckland
(P13, 16/10/08, Clinical Safety stream, 11.10am)Data in Research
This document discusses data in research methodology. It covers different types of data including qualitative and quantitative data. It describes various methods of collecting primary data such as observation, interviews, questionnaires, and schedules. It also discusses methods of collecting secondary data from published and unpublished sources. The importance of data collection in research is explained as well as factors to consider such as the object and scope of inquiry. Measurement scales including nominal, ordinal, interval, and ratio scales are outlined.
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Analysis of Medication Possession Ratio for Improved Blood Pressure Control
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