2 hours ago identify the patient at admission (medical record number). Patient Name – This should be the patient legal name, including surname, given name, middle name or initial. Patient’s Date of Birth – This should be in the format of year, month, and day of birth (entered as – YYYYMMDD). >> Go To The Portal
Relationship of UHDDS to Outpatient Reporting The UHDDS definition of principal diagnosis does not apply to outpatient encounters. In contrast to inpatient coding, no "after study" element is involved because ambulatory care visits do not permit the continued evaluation ordinarily needed to meet UHDDS criteria.
Required for reporting Medicare and Medicaid patients. Many other health care payers also use most of the UHDDS for the uniform billing system. UHDDS requiered data item
The Centers for Disease Control and Prevention issues the UHDDS. Healthcare organizations should assess which code set best meets their needs. Regardless of the categories used, it is highly recommended that individuals be allowed to self-select the category or categories they feel best describe their race and ethnicity.
Many other health care payers also use most of the UHDDS for the uniform billing system. UHDDS requiered data item The UHDDS requires the following items: Principal diagnosis,
The unique number assigned to each patient within a hospital that distinguishes the patient and his or her hospital record from all others in that institution.
The UHDDS guidelines are used by hospitals to report inpatient data elements in a standardized manner. The UHDDS guidelines state all significant procedures are to be reported and a significant procedure is defined as one that is: Surgical in nature, or. Carries a procedural risk, or.
Uniform hospital discharge data setUniform hospital discharge data set (UHDDS)
UHDDS Data ElementsPersonal identification number: health record number.Date of birth.Sex.Race.Ethnicity (Hispanic or Non-Hispanic)Residence: zip code or code for foreign residence.
The UHDDS item #11-b defines Other Diagnoses as “all conditions that coexist at the time of admission, that develop subsequently, or that affect the treatment received and/or the length of stay.
Importance of ICD-10-CM codes ICD-10-CM codes are important because they are more granular than ICD-10 codes and can provide more information about the severity of a patient's condition.
How is the individual patient identified according to the data elements in the UHDDS? patient's residence is identified by the zip code or a code used for foreign residences.
UHDDS. Uniform Hospital Discharge Data Set. used for reporting inpatient data in acute care, short-term care, and long-term care hospitals.
A comorbidity is an additional diagnosis that describes a preexisting condition that because of its presence with a specific principal diagnosis will likely cause an increase in the patient's length of stay in the hospital.
What is the essential clinical dataset? In a nutshell, the ECD defines the data elements that are essential to be documented for a patient within the EHR so the care team may provide quality care. We realized that the industry needed a standardized dataset that provides essential elements for EHR documentation.
What are the components of AHIMA's principles of information governance? Accountability and integrity. Data stewardship is defined as principles and practices established to ensure the knowledgeable and appropriate use of data derived from individuals' personal health information.
Medicare administrative data or Medicare Fee-for-Service claims (administrative) data, also known as health services utilization data, are collected by the Centers for Medicare and Medicaid Services (CMS) and derived from reimbursement information or the payment of bills.
UHDDS Today Hospital or facility identification number or code. Expected insurance payer number or code. Sex, age, and race of the patient. Significant medical procedures performed.
Assigning secondary or “other” diagnoses was a source of confusion in ICD-9 and remains so in ICD-10 today. The Uniform Hospital Discharge Data Set, or UHDDS, is used for reporting inpatient data in acute-care, short-term care, and long-term care hospitals.
POA in medical coding includes any condition that develops during any outpatient encounter. This can include the emergency department, observation, and outpatient surgery.
A POA indicator is assigned to principal and secondary diagnoses (as defined in Section II of the Official Guidelines for Coding and Reporting) and the external cause of injury codes. Issues related to inconsistent, missing, conflicting or unclear documentation must still be resolved by the provider.
Beginning in the early 1990s, HCUP began a voluntary collaboration with SDOs to leverage their data collection efforts to build uniformly formatted national and state hospital encounter-level datasets for research (AHRQ 2015). HCUP greatly expands the availability and use of the statewide discharge data. The AHRQ statewide discharge and visit-level databases include the State Inpatient Databases, the State Emergency Department Databases, and the State Ambulatory Surgery and Services Databases. After receiving the data from SDOs, HCUP conducts standard data quality checks, creates uniformly formatted files, and, to facilitate research, adds data elements derived from the statewide data and linkages to other data (e.g., AHA Annual Survey). These added data elements include clinical classifications based on the ICD-9-CM (International Classification of Diseases, 9th Edition, Clinical Modification) diagnosis and procedures codes (e.g., Clinical Classification Software and comorbidity measures) and sociodemographic indicators based on patient zip code (e.g., urban–rural measures and median income of the patient’s zip code, in quartiles). HCUP also provides linkable files for some states, such as hospital characteristics (from linkage to the American Hospital Annual Survey), cost-to-charge ratios (for each hospital using information derived from Medicare Cost Reports), hospital market structure files (for studies on competition and market forces), and revisit variables (to examine readmissions). HCUP samples the SID and SEDD to create three databases for national estimates—National (Nationwide) Inpatient Sample (NIS) for estimates of inpatient care, Kid’s Inpatient Database (KID) for estimates of inpatient care for children, and Nationwide Emergency Department Sample for estimates of ED visits.1
ICD-10 implementation will significantly affect the HDD. The AHRQ grants, information technology advances, payment policy changes, and the need for outpatient information may stimulate other statewide HDD changes. To remain a mainstay of health services research, statewide HDD need to keep pace with changing user needs while minimizing collection burdens.
Schoenman et al. (2007) described the limitations as falling into three types: quality of data elements, missing data elements, and excluded populations. One data quality problem they identified concerns the accuracy of some ICD-9-CM-coded diagnoses and procedures, including miscoding and omission of comorbidities. O’Malley et al. (2005) noted a number of factors that may affect the data quality of the ICD-9-CM diagnoses, including the quality of information in the medical record, coder training and experience, facility quality control, and unintentional and intentional coding errors. Incentives to maximize reimbursement and to code only clinical information that affects reimbursement may affect the discharge data. Another shortcoming is that the maximum number of diagnosis and procedure fields is limited on some state’s data. For example in 2012, some SDOs had a maximum of 9 or 10 diagnoses, while most had a maximum of 20 or more (Coffey et al. 2015). Thus, some states may have incomplete information for the more complex cases, affecting analyses (Iezzoni et al. 1992; Romano and Mark 1994). Another data quality concern mentioned by SDOs (Barrett et al. 2014) concerns expected payer, particularly that Medicaid enrollees in managed care may be miscoded as privately insured (Chattopadhyay and Bindman 2005). Data quality also suffers for multistate analyses when states collect data elements differently (Coffey et al. 1997), such as collecting different categories for expected payer categories (Barrett et al. 2014) or for race–ethnicity (Geppert et al. 2004; Andrews 2011).
There are several reasons for their wide availability and use. Because existing claims data are their foundation, the resources to create the datasets are modest when compared to primary data collection such as surveys or medical record abstraction. Using the hospital claims, standard format (Uniform Bill) minimizes the burden on hospitals to report data, on states to process incoming files, and on analysts to use the datasets. The datasets contain core data elements that are valuable for many types of analyses and applications. Analyses can be done at various levels of aggregation, including discharge/visit, patient (in some states with encrypted identifiers), hospital, physician (in some states), and geographic areas (zip code, county, state, region, nation). The datasets include records for all-payers, including the uninsured, and generally include all non-Federal acute care hospitals in a state. AHRQ’s HCUP transforms the statewide data into uniform research files to facilitate multistate analyses. HCUP also develops national datasets from these data for national estimates (AHRQ 2015). Numerous software and standard methodologies are available to facilitate analysis, including clinical groupers, risk adjustment/severity of illness methodologies, quality of care measures, and economic measures. Online query systems provide for easy access to statistics generated from the data. Linkages to other datasets (through hospital, geographic, patient, or physician identifiers) expand the analytic capacity of statewide data.
Hospitals, their consultants, and other private entities use the data to provide hospitals and health plans information on the business and financial side of hospital services . Hospitals are interested in patient flow and market share analyses—both their own and other hospitals in their market area, and efficiency assessments such as case mix- or severity-adjusted length of stay.
Beyond the public comparative reports, hospitals use the information for quality assessments and internal improvements. The statewide data provide hospitals with information on their own performance as well as benchmarks on their peers.
States use the data to develop all-payer public reports on hospitals. In some cases, this is very basic information such as caseloads for specific conditions and surgeries, length of stay, and charges. In some states, the reports are more sophisticated, involving complex analyses to provide risk-adjusted outcomes, such as mortality and readmissions.
POA indicator reporting is mandatory for all claims involving inpatient admissions to general acute care hospitals or other facilities. POA is defined as present at the time the order for inpatient admission occurs.
Conditions that develop during an outpatient encounter, including emergency department, observation, or outpatient surgery, are considered POA. A POA Indicator must be assigned to principal and secondary diagnoses (as defined in Section II of the Official Guidelines for Coding and Reporting) and the external cause of injury codes.
On June 13, 2008 Change Request (CR) 6086 was released. This CR instructs on correct POA Indicator reporting options and instructs the Grouper to not apply HAC logic to claims from exempt Inpatient hospitals. CR 6086 is available in the Statute/Regulations/Program Instructions section, accessible through the navigation menu at left.
On October 1, 2007, all Inpatient Prospective Payment System (IPPS) Hospitals were required to begin submitting Present on Admission (POA) Indicator information for all principal and secondary diagnoses. Instructions on how to report the appropriate POA indicator are included in the Official Guidelines for coding and Reporting found under the Related Links section below.
CMS does not require a POA Indicator for an external cause of injury code unless it is being reported as an "other diagnosis.". Issues related to inconsistent, missing, conflicting, or unclear documentation must be resolved by the provider.
In the 2002 landmark study Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, the Institute of Medicine documented evidence that race and ethnicity are significant predictors of the quality of care, observing that minorities who had the same insurance, status, and income as nonminorities received a lower quality of care. 1
The Health Research and Educational Trust (HRET), an affiliate of the American Hospital Association, offers providers a toolkit for the systematic collection of data used to assess healthcare disparities ( www.hretdisparities.org ). The toolkit is an excellent resource for organizations seeking to standardize and improve their data collection processes.
Collecting accurate equity data supports efforts to reduce healthcare disparities and create equal care for all.
At a minimum, HRET recommends the following data be collected in settings that measure and manage quality: race and ethnicity, language, and socioeconomic status.
The Agency for Healthcare Research and Quality, the Centers for Medicare and Medicaid Services, and state public health entities have ongoing initiatives to address healthcare disparities. Accrediting agencies are also focusing on aspects of care that could be associated with disparities.
In that study IOM described racial and ethnic healthcare disparities as racial or ethnic differences in the quality of healthcare that are not due to access-related factors or clinical needs, preferences, and appropriateness of intervention. Other studies and reports have demonstrated a similar relationship between healthcare disparities and the quality of healthcare.
Analyze race and ethnicity indicator data to determine if the data sets are properly utilized; for example, overutilization of the racial category “unknown.” Analyze a sample of each minority racial category to determine if patients are being properly interviewed.