- Data For Electrophysiologic Testing per 1000 Medicare Enrollees 2012
- Electrophysiologic Testing for Medicare Enrollees 2012
- Electrophysiologic Testing for Medicare Enrollees
- Dartmouth Atlas Surveys
- Evidence-Based Decisions Electrophysiologic Testing
- Claims-Based Analyses Electrophysiologic Testing
- Electrophysiologic Testing
- EPS Data
- EPS HRR Level Data
- Medicare Enrollees Data
Electrophysiologic Testing per 1000 Medicare Enrollees HRR Level
This dataset contains data related to electrophysiologic testing per 1,000 Medicare enrollees. Data is for 2012 at the HRR level. Rates are adjusted for age, sex, and race.
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This dataset is one of the surveys carried out by Dartmouth Atlas under the umbrella “Effective Care”. Effective care refers to services that are of proven value and have no significant tradeoffs — that is, the benefits of the services so far outweigh the risks that all patients with specific medical needs should receive them. These services, such as beta-blockers for heart attack patients, are backed by well-articulated medical theory and strong evidence of efficacy, determined by clinical trials or valid cohort studies. Failure to provide effective care can lead to serious consequences; for example, amputation of a leg is an infrequent but devastating complication of peripheral vascular disease and diabetes.
The claims-based analyses of effective care focus on either the entire fee-for-service Medicare population eligible for both Part A and B and between the ages of 65 and 99 or a subset of that population at risk for a specific procedure or service. For example, the analysis of amputations examines the entire Medicare population, while the analyses of testing among diabetics are restricted to Medicare beneficiaries between the ages of 65 and 75 with a diagnosis of diabetes. When appropriate, statistical adjustments are carried out to account for differences in age, race and sex.
Evidence-based decisions, performance assessment, and explicit efforts to improve quality, reduce errors, and involve patients in care decisions are often components of high quality health care. Such care requires providers, health systems, and others to work together to improve health outcomes and patient satisfaction while containing costs.
Despite efforts towards higher quality care, an estimated 30% of patients did not receive recommended preventive care or treatment in 2009. Poor care coordination within and among facilities can lead to poor health outcomes and readmissions; about 20% of discharged elderly patients return to the hospital within 30 days. Hospital acquired infections killed about 100,000 Americans in 2007, and between 44,000 and 98,000 Americans are estimated to die from medical errors each year.
Quality varies widely by state, race, ethnicity, and income. Blacks, Hispanics, American Indians and those with low incomes often get lower quality care than non-Hispanic whites and those with high incomes. One study found that women and minorities get lower quality care than their counterparts even when insurance status, income, and condition are accounted for.
Even with the highest per capita health care spending in the world, the US has shorter lifespans and higher infant mortality rates than other wealthy nations. Several studies estimate that at least 30% of US health expenditures are on practices and procedures that do not improve health. Preventable hospitalizations cost $26 billion in 2009, and in 2008, medical errors cost nearly $20 billion.
Adopting and implementing initiatives to improve the quality of health care in all settings can help us all get the care we need when we need it, leading to longer, healthier lives, and healthier, more productive communities.
About this Dataset
John Snow Labs; The Dartmouth Institute for Health Policy and Clinical Practice;
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Electrophysiologic Testing for Medicare Enrollees, Dartmouth Atlas Surveys, Evidence-Based Decisions Electrophysiologic Testing, Claims-Based Analyses Electrophysiologic Testing, Electrophysiologic Testing, EPS Data, EPS HRR Level Data, Medicare Enrollees Data
Data For Electrophysiologic Testing per 1000 Medicare Enrollees 2012, Electrophysiologic Testing for Medicare Enrollees 2012
|Survey_Year||Year for which the survey data was conducted||date||required : 1|
|Event_Abbreviation||Abbreviation of the event of which the data was collected i.e, Electrophysiologic Testing||string||required : 1|
|Event_Stratification||Stratification of the event, "All" in this case||string||required : 1|
|Location_ID||ID assigned to the data location||integer||level : Nominalrequired : 1|
|State_Abbreviation||State Abbreviation as well as name of the location.||string||required : 1|
|Denominator||Number of Medicare fee-for-service enrollees||integer||level : Ratiorequired : 1|
|Observed_Individuals||Number of individuals observed during the survey||integer||level : Ratio|
|Observed_Events||Number of events observed during the survey||integer||level : Ratio|
|Expected_Events||Number of events expected during the survey||number||level : Ratio|
|Observed_Expected_Event_Ratio||Ratio of observed events to expected events||number||level : Ratio|
|Crude_Rate||A measure of overall frequency which has not been adjusted for significant factors which might have influenced the rate. (age, sex and race in this case)||number||level : Ratio|
|Adjusted_Rate||Rates are adjusted for age, sex and race using the indirect method, using the U.S. Medicare population as the standard.||number||level : Ratio|
|Standard_Error||The standard error (SE) is a measure of the amount the statistic may be expected to differ by chance from the true value of the statistic. The larger the SE, the less sure you can be that if you took a different sample and computed the statistic again, that it would be close to the statistic you computed from the first sample.||number||level : Ratio|
|Lower_Confidence_Interval||95% confidence interval lower limit||number||level : Ratio|
|Upper_Confidence_Interval||95% confidence interval upper limit.||number||level : Ratio|
|Population_Unit||Population Unit taken for the survey analysis||integer||level : Ratio|
|Location_Type||Type of location. "HRR" in this case. "HRR" or "Hospital Referral Regions" represents regional health care markets for tertiary medical care that generally requires the services of a major referral center.||string||required : 1|
|Suppression_Limit||Suppression limit of the event||integer||level : Ratiorequired : 1|
|Precision_Limit||Precision limit of the event||integer||level : Ratiorequired : 1|
|Statistically_Insignificant_Value||Value assigned to statistically insignificant data||integer||level : Ratio|
|Survey Year||Event Abbreviation||Event Stratification||Location ID||State Abbreviation||Denominator||Observed Individuals||Observed Events||Expected Events||Observed Expected Event Ratio||Crude Rate||Adjusted Rate||Standard Error||Lower Confidence Interval||Upper Confidence Interval||Population Unit||Location Type||Suppression Limit||Precision Limit||Statistically Insignificant Value|
|2012||ElTe||ALL||14||AZ- SUN CITY||45381||282||305||96.287||3.168||6.721||6.629||0.335||5.9239999999999995||7.417000000000001||1000||hrr||11||26|