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595 Results
This dataset contains the partners working on prevention agenda priority and focus areas. The dataset is organized by county, priority area and focus area. Each partner’s address, phone number and in many cases e-mail contact are provided.The Prevention Agenda 2013-17 is New York State’s health improvement plan for 2013 through 2017. This plan involves a unique mix of organizations including local health departments, health care providers, health plans, community based organizations, advocacy groups, academia, employers as well as state agencies, schools, and businesses whose activities can influence the health of individuals and communities and address health disparities. This unprecedented collaboration is designed to demonstrate how communities across the state can work together to improve the health and quality of life for all New Yorkers. The purpose of the dataset is to provide the public, health providers and tentative DOH partners with some basic information about who in NYS is working on prevention agenda related items.
Updated
March 22 2018
Views
30,691
This dataset includes Medicaid clinical metrics for Clinical Improvement Projects (Domain 3) of the Delivery System Reform Incentive Payment (DSRIP) Program. The DSRIP program will promote community-level collaborations and focus on system reform, specifically a goal to achieve a 25 percent reduction in avoidable hospital use over five years. As a part of the DSRIP program, Performing Provider System will employ multiple projects both to transform health care delivery as well as to address the broad needs of the population that the performing provider system serves. These projects described in Attachment J are grouped into different strategies, such as behavioral health, within each Domain (System Transformation Projects (Domain 2), Clinical Improvement Projects (Domain 3), and Population-wide Projects (Domain 4). For each strategy, there is a set of metrics that the performing provider system will be responsible for if they do any one of the projects within that strategy. This dataset includes only Domain 3 Clinical Metrics.
Updated
March 22 2018
Views
18,331
The dataset contains Potentially Preventable Readmission observed, expected, and risk adjusted rates by hospital for all payer beneficiaries beginning in 2009.
Updated
March 22 2018
Views
41,283
This dataset is a compilation of easy tips to prevent type 2 diabetes. They were compiled from several documents produced by the National Diabetes Education Program (NDEP). NDEP is a partnership of the National Institutes of Health, the Centers for Disease Control and Prevention, and more than 200 public and private organizations.
Updated
March 22 2018
Views
12,166
This dataset contains information on the names and locations of evidence-based self-management programs delivered by QTAC-NY partners, including street address, city, state, zip code, county, and Delivery System Reform Incentive Payment (DSRIP) Program region. It also contains information about the capacity of each implementation site to deliver specific self-management programs. The data will be updated on a yearly basis.
Updated
March 22 2018
Views
39,901
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.
Updated
March 22 2018
Views
11,024
The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for IQIs generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ).
The IQIs are a set of measures that provide a perspective on hospital quality of care using hospital administrative data. These indicators reflect quality of care inside hospitals and include inpatient mortality for certain procedures and medical conditions; utilization of procedures for which there are questions of overuse, underuse, and misuse; and volume of procedures for which there is some evidence that a higher volume of procedures is associated with lower mortality.
All the IQI 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) IQI measures.
The IQIs are a set of measures that provide a perspective on hospital quality of care using hospital administrative data. These indicators reflect quality of care inside hospitals and include inpatient mortality for certain procedures and medical conditions; utilization of procedures for which there are questions of overuse, underuse, and misuse; and volume of procedures for which there is some evidence that a higher volume of procedures is associated with lower mortality.
All the IQI 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) IQI measures.
The mortality, volume and utilization measures IQIs are presented by hospital as rates or counts. Area-level utilization measures are presented by county as rates.
Updated
February 16 2018
Views
11,532
File or Document
This print image version of the Institutional Cost Report (ICR) has been audited by the DOH. is the Institutional Cost Report (ICR) is a uniform report completed by New York hospitals to report income, expenses, assets, liabilities, and statistics to the Department of Health (DOH). Under DOH regulations, (Part 86-1.2), Article 28 hospitals are required to file financial and statistical data with DOH annually. The data filed is part of the ICR and is received electronically through a secured network. This data is used to develop Medicaid rates, assist in the formulation of reimbursement methodologies, and analyze trends. For more information, check out: http://www.health.ny.gov/facilities/hospital/
Updated
February 9 2018
Views
1,536
File or Document
This version of the Institutional Cost Report (ICR) has been audited by a Certified Public Accounting Firm. The ICR is a uniform report completed by New York State hospitals to report income, expenses, assets, liabilities, and statistics to the Department of Health (DOH). Under DOH regulations, (Part 86-1.2), Article 28 hospitals are required to file financial and statistical data with DOH annually. The data filed is part of the ICR and is received electronically through a secured network. This data is used to develop Medicaid rates, assist in the formulation of reimbursement methodologies, and analyze trends. For more information, check out: http://www.health.ny.gov/facilities/hospital/index.htm
Updated
February 9 2018
Views
2,336
File or Document
This print image version of the Institutional Cost Report (ICR) has been audited by the DOH. is the Institutional Cost Report (ICR) is a uniform report completed by New York hospitals to report income, expenses, assets, liabilities, and statistics to the Department of Health (DOH). Under DOH regulations, (Part 86-1.2), Article 28 hospitals are required to file financial and statistical data with DOH annually. The data filed is part of the ICR and is received electronically through a secured network. This data is used to develop Medicaid rates, assist in the formulation of reimbursement methodologies, and analyze trends. For more information, check out: http://www.health.ny.gov/facilities/hospital/index.htm
Updated
February 8 2018
Views
1,446
File or Document
This version of the Institutional Cost Report (ICR) has been audited by a Certified Public Accounting Firm. The ICR is a uniform report completed by New York State hospitals to report income, expenses, assets, liabilities, and statistics to the Department of Health (DOH). Under DOH regulations, (Part 86-1.2), Article 28 hospitals are required to file financial and statistical data with DOH annually. The data filed is part of the ICR and is received electronically through a secured network. This data is used to develop Medicaid rates, assist in the formulation of reimbursement methodologies, and analyze trends. For more information, check out: http://www.health.ny.gov/facilities/hospital/index.htm
Tags
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Updated
February 8 2018
Views
1,352
The primary purpose for the Provider Network Data System is to collect data needed to evaluate the provider networks including physicians, hospitals, labs, home health agencies, durable medical equipment providers, etc., for all types of health plans in New York State. Beginning in 2017, the PNDS includes Medicaid Managed Care (MMC), HIV Special Need Plans (SNP), Health and Recovery Plans (HARP), Child Health Plus (CHP), Programs of All-Inclusive Care for the Elderly (PACE), Non-PACE Managed Long-Term Care (MLTC) plans, Qualified Health Plans (QHP), Essential Plans (EP), and commercial plans. This dataset reflects institutional provider data. Provider Network Data System information is self-reported by health plans. The PNDS data dictionary can be found at http://www.health.ny.gov/health_care/managed_care/docs/dictionary.pdf . To use the NYS Provider & Health Plan Look-Up Tool, click on the following link: https://pndslookup.health.ny.gov/ .
Updated
January 25 2018
Views
1,479
The dataset groups 65 individual Potentially Preventable Complication (PPC) measures into 8 different categories, providing observed and risk-adjusted rates 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.
Updated
January 24 2018
Views
3,568
The dataset shows each of the 65 individual Potentially Preventable Complication (PPC) measures, providing observed and risk-adjusted rates 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.
Updated
January 24 2018
Views
3,965
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.
Updated
January 24 2018
Views
41,159
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.
Updated
January 24 2018
Views
50,387
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).
Updated
January 24 2018
Views
42,244
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.
Updated
January 24 2018
Views
44,992
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.
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.
Updated
January 24 2018
Views
44,495
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 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.
Updated
January 24 2018
Views
44,483
File or Document
The primary purpose for the Provider Network Data System is to collect data needed to evaluate the provider networks including physicians, hospitals, labs, home health agencies, durable medical equipment providers, etc., for all types of health plans in New York State. Beginning in 2017, the PNDS includes Medicaid Managed Care (MMC), HIV Special Need Plans (SNP), Health and Recovery Plans (HARP), Child Health Plus (CHP), Programs of All-Inclusive Care for the Elderly (PACE), Non-PACE Managed Long-Term Care (MLTC) plans, Qualified Health Plans (QHP), Essential Plans (EP), and commercial plans (commercial plan reporting will be incomplete until Q2 2017). This dataset reflects individual provider data. Provider Network Data System information is self-reported by health plans. The PNDS data dictionary can be found at http://www.health.ny.gov/health_care/managed_care/docs/dictionary.pdf . To use the NYS Provider & Health Plan Look-Up Tool, click on the following link, https://pndslookup.health.ny.gov/ .
Updated
January 23 2018
Views
2,299
File or Document
The primary purpose for the Provider Network Data System is to collect data needed to evaluate the provider networks including physicians, hospitals, labs, home health agencies, durable medical equipment providers, etc., for all types of health plans in New York State. Beginning in 2017, the PNDS includes Medicaid Managed Care (MMC), HIV Special Need Plans (SNP), Health and Recovery Plans (HARP), Child Health Plus (CHP), Programs of All-Inclusive Care for the Elderly (PACE), Non-PACE Managed Long-Term Care (MLTC) plans, Qualified Health Plans (QHP), Essential Plans (EP), and commercial plans (commercial plan reporting will be incomplete until Q2 2017). This dataset reflects institutional provider data. Provider Network Data System information is self-reported by health plans. The PNDS data dictionary can be found at http://www.health.ny.gov/health_care/managed_care/docs/dictionary.pdf. To use the NYS Provider & Health Plan Look-Up Tool, click on the following link, https://pndslookup.health.ny.gov/.
Updated
January 23 2018
Views
1,615
This chart displays the total number of inpatient discharges by the patient county of residence and discharge year for the counties with the largest number of discharges. To view a county that is not included in the initial visualization, explore the different filter conditions. To expand the view from the initial categories shown, you may uncheck the preselected filter and choose your own parameters.
The chart is based on data collected on patients and hospital discharges in the Statewide Planning and Research Cooperative System (SPARCS).
The SPARCS data has been divided into two distinct datasets, Hospital Discharges by Patient County of Residence and Hospital Discharges by Facility to preserve the confidentiality of identifiable individual information.
This dataset does not include facility names.
For additional information, check out http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.
Updated
January 19 2018
Views
25,893
This dataset represents self‐reported performance data by HIV ambulatory care programs. All HIV ambulatory programs throughout New York State with a significant HIV caseload (a total caseload of at least 30 HIV‐infected patients receiving ambulatory HIV care at one or more sites) are expected to self‐report their annual quality of care performance data using standardized submission tools and methodologies. With the assistance of the online eHIVQUAL application, performance data results are instantly available to HIV programs, allowing them to immediately utilize their data findings to prioritize upcoming quality activities, and are available for generating benchmarking reports across New York State. See Limitations regarding redaction of small‐population data.
Updated
January 4 2018
Views
58,028
Note: This dataset is no longer being updated. For continued updates related to this information, please visit: https://www.health.ny.gov/statistics/opioid/#i_one. This dataset is a summary of inpatient discharges and outpatient visit data for opioid-related diagnosis. The data reflects selected diagnosis codes that include a range of poisonings by opiates, opium, heroin, methadone, and other related narcotics. Please refer to the data dictionary in the “About” section for information on the current data available.
Updated
January 2 2018
Views
10,644
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