This line chart compares the median cost vs. median charge for cellulitis & other bacterial skin Infections with a moderate severity of illness by hospital. The dataset contains information submitted by New York State Article 28 Hospitals as part of the New York Statewide Planning and Research Cooperative (SPARCS) and Institutional Cost Report (ICR) data submissions. The dataset contains information on the volume of discharges, All Payer Refined Diagnosis Related Group (APR-DRG), the severity of illness level (SOI), medical or surgical classification the median charge, median cost, average charge and average cost per discharge. When interpreting New York’s data, it is important to keep in mind that variations in cost may be attributed to many factors. Some of these include overall volume, teaching hospital status, facility specific attributes, geographic region and quality of care provided.For more information, check out: http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

This line chart compares the median cost vs. median charge for normal newborn or neonate w other problem with minor severity of illness by hospital. The dataset contains information submitted by New York State Article 28 Hospitals as part of the New York Statewide Planning and Research Cooperative (SPARCS) and Institutional Cost Report (ICR) data submissions. The dataset contains information on the volume of discharges, All Payer Refined Diagnosis Related Group (APR-DRG), the severity of illness level (SOI), medical or surgical classification the median charge, median cost, average charge and average cost per discharge. When interpreting New York’s data, it is important to keep in mind that variations in cost may be attributed to many factors. Some of these include overall volume, teaching hospital status, facility specific attributes, geographic region and quality of care provided.For more information, check out: http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

This dataset is a summary of hospital inpatient discharges from every hospital including Article 28 facilities; Ambulatory Surgery and Emergency Department visits, from the Statewide Planning and Research Cooperative System (SPARCS). The data is rolled up by patient county of residence. This dataset was modified in September 2013 to be in sync with the release of the Hospital Inpatient Discharges (SPARCS De-Identified) datasets. Please refer to the data dictionary for information on the current data available.

The SPARCS data has been divided into two distinct datasets: Hospital Discharges by Patient County of Residence and Hospital Discharges by Facility. This is to preserve the confidentiality of identifiable individual information.This dataset does not include facility names.

This line chart shows the observed vs. expected Potentially Preventable Complication (PPC) rates for all payer beneficiaries by hospital.

The chart is based on a dataset that contains Potentially Preventable Complications (PPC) observed, expected, and risk-adjusted rates for all payer beneficiaries by hospital beginning in 2009. The Potentially Preventable Complications (PPC), obtained from
software created by 3M Health Information Systems, are
harmful events or negative outcomes that develop after hospital
admission and may result from processes of care and treatment
rather than from natural progression of the underlying illness
and are therefore potentially preventable.
The rates were calculated using Statewide Planning and
Research Cooperative System (SPARCS) inpatient data.
The observed, expected and risk adjusted rates for PPC are
presented by hospital (including a statewide total).

For more information, check out:
http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset..

This line chart compares the median cost vs. median charge for major depressive disorders & other unspecified psychoses with a moderate severity of illness by hospital. The dataset contains information submitted by New York State Article 28 Hospitals as part of the New York Statewide Planning and Research Cooperative (SPARCS) and Institutional Cost Report (ICR) data submissions. The dataset contains information on the volume of discharges, All Payer Refined Diagnosis Related Group (APR-DRG), the severity of illness level (SOI), medical or surgical classification the median charge, median cost, average charge and average cost per discharge. When interpreting New York’s data, it is important to keep in mind that variations in cost may be attributed to many factors. Some of these include overall volume, teaching hospital status, facility specific attributes, geographic region and quality of care provided.For more information, check out: http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

This line chart compares the median costs vs. median charges for renal failure with a major severity of illness by hospital. The dataset contains information submitted by New York State Article 28 Hospitals as part of the New York Statewide Planning and Research Cooperative (SPARCS) and Institutional Cost Report (ICR) data submissions. The dataset contains information on the volume of discharges, All Payer Refined Diagnosis Related Group (APR-DRG), the severity of illness level (SOI), medical or surgical classification the median charge, median cost, average charge and average cost per discharge. When interpreting New York’s data, it is important to keep in mind that variations in cost may be attributed to many factors. Some of these include overall volume, teaching hospital status, facility specific attributes, geographic region and quality of care provided.For more information, check out: http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

The charts shows risk adjusted rates of Potentially Preventable Readmissions by hospital for all payers beginning in 2009.
The Potentially Preventable Readmission (PPR) software created by 3M Health Information Systems, identifies hospital admissions clinically related to an initial admission within a specified time period. For this dataset, readmissions were evaluated within a 30-day time period from the discharge date of the initial hospital admission. A PPR may have resulted from a deficiency in the process of care and treatment at the initial hospitalization or lack of post discharge follow up. PPRs are not defined by unrelated events that occur post-discharge, such as admissions for trauma.
For each hospital, the total number of at risk admissions, the total number of observed PPR chains, the observed PPR rate, the expected PPR rate, and risk adjusted PPR rate are presented by year. For more information, check out http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

The charts shows observed vs. expected Potentially Preventable Readmission rates by hospital for all payer beneficaries.
The Potentially Preventable Readmission (PPR) software created by 3M Health Information Systems, identifies hospital admissions clinically related to an initial admission within a specified time period. For this dataset, readmissions were evaluated within a 30-day time period from the discharge date of the initial hospital admission. A PPR may have resulted from a deficiency in the process of care and treatment at the initial hospitalization or lack of post discharge follow up. PPRs are not defined by unrelated events that occur post-discharge, such as admissions for trauma.
For each hospital, the total number of at risk admissions, the total number of observed PPR chains, the observed PPR rate, the expected PPR rate, and risk adjusted PPR rate are presented by year. For more information, check out http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

This chart shows risk adjusted rates per 10,000 discharges of Potentially Preventable Complications (PPC) for all payer beneficiaries by hospital.
The chart is based on a dataset that contains Potentially Preventable Complications (PPC) observed, expected, and risk-adjusted rates for all payer beneficiaries by hospital beginning in 2009.

The Potentially Preventable Complications (PPC), obtained from software created by 3M Health Information Systems, are harmful events or negative outcomes that develop after hospital admission and may result from processes of care and treatment rather than from natural progression of the underlying illness and are therefore potentially preventable.

The rates were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data.

The observed, expected and risk adjusted rates for PPC are presented by hospital (including a statewide total). For more information, check out:
http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

The Statewide Planning and Research Cooperative System (SPARCS) Inpatient De-identified File contains discharge level detail on patient characteristics, diagnoses, treatments, services, and charges. This data file contains basic record level detail for the discharge. The de-identified data file does not contain data that is protected health information (PHI) under HIPAA. The health information is not individually identifiable; all data elements considered identifiable have been redacted. For example, the direct identifiers regarding a date have the day and month portion of the date removed.

The dataset contains Potentially Preventable Readmission observed, expected, and risk adjusted rates by hospital for all payer beneficiaries beginning in 2009.

The dataset shows Potentially Preventable Complication (PPC) measures for the 36 major PPCs combined; providing observed, expected, and risk-adjusted rates and counts for all payer discharges by hospital and statewide, beginning in 2013.

Potentially Preventable Complications (PPC), obtained from software created by 3M Health Information Systems, are defined as harmful events or negative outcomes that develop or occur during hospitalization and may result from processes of care and treatment rather than from natural progression of the underlying illness.

The PPCs were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data.

This is one of two datasets that contain observed and expected rates for Agency for Healthcare Research and Quality Prevention Quality Indicators – Adult (AHRQ PQI) beginning in 2009. This dataset is at the county level. The Agency for Healthcare Research and Quality (AHRQ) Prevention Quality Indicators (PQIs) are a set of population based measures that can be used with hospital inpatient discharge data to identify ambulatory care sensitive conditions. These are conditions where 1) the need for hospitalization is potentially preventable with appropriate outpatient care, or 2) conditions that could be less severe if treated early and appropriately. All PQIs apply only to adult populations (over the age of 18 years). The rates were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data and Claritas population information.

The observed rates and expected rates for each AHRQ PQI is presented by either resident county (including a statewide total) or resident zip code (including a statewide total).

The Statewide Planning and Research Cooperative System (SPARCS) Inpatient De-identified File contains discharge level detail on patient characteristics, diagnoses, treatments, services, and charges.

This data file contains basic record level detail for the discharge. The de-identified data file does not contain data that is protected health information (PHI) under HIPAA. The health information is not individually identifiable; all data elements considered identifiable have been redacted. For example, the direct identifiers regarding a date have the day and month portion of the date removed.

For more information visit: https://www.health.ny.gov/statistics/sparcs/

This chart shows the overall risk adjusted rate per 100,000 for hospital inpatient prevention quality indicators (all payers) for pediatric discharges by county and year. The dataset contains observed, expected, and risk-adjusted rates for Agency for Healthcare Research and Quality Pediatric Quality Indicators – Pediatric (AHRQ PDI) beginning in 2009.

The Agency for Healthcare Research and Quality (AHRQ) Pediatric Quality Indicators (PDIs) are a set of population based measures that can be used with hospital inpatient discharge data to identify ambulatory care sensitive conditions. These are conditions where 1) the need for hospitalization is potentially preventable with appropriate outpatient care, or 2) conditions that could be less severe if treated early and appropriately. Both the Urinary Tract Infection and Gastroenteritis PDIs include admissions for patients aged 3 months through 17 years. The asthma PDI includes admissions for patients aged 2 through 17 years. Eligible admissions for the Diabetes Short-term Complications PDI includes admissions for patients aged 6 through 17 years.

The rates were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data and Claritas population information.

The observed, expected, risk-adjusted rates, and difference in rates, for each AHRQ PDI are presented by either resident county (including a statewide total). To view the data presented by resident zip code (including a statewide tota), go to: https://health.data.ny.gov/Health/Hospital-Inpatient-Prevention-Quality-Indicators-P/2xc5-n3zd. For more information, check out: http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

The datasets contain Potentially Preventable Visit (PPV) observed, expected, and risk-adjusted rates for all payer beneficiaries by patient county and patient zip code beginning in 2011.

The Potentially Preventable Visits (PPV), obtained from software created by 3M Health Information Systems, are emergency visits that may result from a lack of adequate access to care or ambulatory care coordination. These ambulatory sensitive conditions could be reduced or eliminated with adequate patient monitoring and follow up.

The rates were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient and outpatient data and Claritas population information.

The observed, expected and risk adjusted rates for PPV are presented by either resident county (including a statewide total) or resident zip code (including a statewide total). For more information, check out: http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

The datasets contain Potentially Preventable Visit (PPV) observed, expected, and risk-adjusted rates for all payer beneficiaries by patient county and patient zip code beginning in 2011.

The Potentially Preventable Visits (PPV), obtained from software created by 3M Health Information Systems, are emergency visits that may result from a lack of adequate access to care or ambulatory care coordination. These ambulatory sensitive conditions could be reduced or eliminated with adequate patient monitoring and follow up.

The rates were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient and outpatient data and Claritas population information.

The observed, expected and risk adjusted rates for PPV are presented by either resident county (including a statewide total) or resident zip code (including a statewide total).

The Statewide Planning and Research Cooperative System (SPARCS) Inpatient De-identified File contains discharge level detail on patient characteristics, diagnoses, treatments, services, and charges. This data file contains basic record level detail for the discharge. The de-identified data file does not contain data that is protected health information (PHI) under HIPAA. The health information is not individually identifiable; all data elements considered identifiable have been redacted. For example, the direct identifiers regarding a date have the day and month portion of the date removed.

The Statewide Planning and Research Cooperative System (SPARCS) Inpatient De-identified File contains discharge level detail on patient characteristics, diagnoses, treatments, services, and charges. This data file contains basic record level detail for the discharge. The de-identified data file does not contain data that is protected health information (PHI) under HIPAA. The health information is not individually identifiable; all data elements considered identifiable have been redacted. For example, the direct identifiers regarding a date have the day and month portion of the date removed.

The dataset contains observed, expected, and risk-adjusted rates for the Agency for Healthcare Research and Quality Pediatric Quality Indicators – Pediatric (AHRQ PDI) beginning in 2009.
The AHRQ PDIs are a set of population based measures that can be used with hospital inpatient discharge data to identify ambulatory care sensitive conditions. These are conditions where 1) the need for hospitalization is potentially preventable with appropriate outpatient care, or 2) conditions that could be less severe if treated early and appropriately. Both the Urinary Tract Infection and Gastroenteritis PDIs include admissions for patients aged 3 months through 17 years. The asthma PDI includes admissions for patients aged 2 through 17 years. Eligible admissions for the Diabetes Short-term Complications PDI includes admissions for patients aged 6 through 17 years.

The rates were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data and Claritas population information.

The observed, expected, risk-adjusted rates, and difference in rates, for each AHRQ PDI are presented by resident county (including a statewide total).

The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for Patient Safety Indicators generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ).

The PSIs are a set of indicators providing information on potential in hospital complications and adverse events following surgeries, procedures, and childbirth. The PSIs were developed by AHRQ after a comprehensive literature review, analysis of ICD-9-CM codes, review by a clinician panel, implementation of risk adjustment, and empirical analyses.

All PSI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) PSI measures.

The mortality, volume and utilization measures PSIs are presented by hospital as rates or counts. Area-level measures are presented by county as rates.

The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for Patient Safety Indicators generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ).

The PSIs are a set of indicators providing information on potential in hospital complications and adverse events following surgeries, procedures, and childbirth. The PSIs were developed by AHRQ after a comprehensive literature review, analysis of ICD-9-CM codes, review by a clinician panel, implementation of risk adjustment, and empirical analyses.

All PSI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) PSI measures.

The mortality, volume and utilization measures PSIs are presented by hospital as rates or counts. Area-level measures are presented by county as rates.

The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for Patient Safety Indicators generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ).

The PSIs are a set of indicators providing information on potential in hospital complications and adverse events following surgeries, procedures, and childbirth. The PSIs were developed by AHRQ after a comprehensive literature review, analysis of ICD-9-CM codes, review by a clinician panel, implementation of risk adjustment, and empirical analyses.

All PSI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) PSI measures.

The mortality, volume and utilization measures PSIs are presented by hospital as rates or counts. Area-level measures are presented by county as rates.

The dataset contains observed, expected, and risk-adjusted rates for the Agency for Healthcare Research and Quality Pediatric Quality Indicators – Pediatric (AHRQ PDI) beginning in 2009. The AHRQ PDIs are a set of population based measures that can be used with hospital inpatient discharge data to identify ambulatory care sensitive conditions. These are conditions where 1) the need for hospitalization is potentially preventable with appropriate outpatient care, or 2) conditions that could be less severe if treated early and appropriately. Both the Urinary Tract Infection and Gastroenteritis PDIs include admissions for patients aged 3 months through 17 years. The asthma PDI includes admissions for patients aged 2 through 17 years. Eligible admissions for the Diabetes Short-term Complications PDI includes admissions for patients aged 6 through 17 years.

The rates were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data and Claritas population information.

The observed, expected, risk-adjusted rates, and difference in rates, for each AHRQ PDI are presented by resident zip code (including a statewide total).

The datasets contain Potentially Preventable Visit (PPV) observed, expected, and risk-adjusted rates for all payer beneficiaries by patient county and patient zip code beginning in 2011. The Potentially Preventable Visits (PPV), obtained from software created by 3M Health Information Systems, are emergency visits that may result from a lack of adequate access to care or ambulatory care coordination. These ambulatory sensitive conditions could be reduced or eliminated with adequate patient monitoring and follow up.