Healthcare Datasets Are Essential For Research And Analysis, Aiding In Essay Writing.

HEDIS

Discuss the healthcare data sets (such as HEDIS, UHDDS, OASIS).

Healthcare dataset can be defined as a set of elements of data with consistent and identical definitions. These are a standard information set obtained from the records of care from the systems or organizations, which captures base data. They are the structured data lists bearing the permissible values, clear label and definitions, classifications and codes. From these datasets, information is compiled and derived for the improvement and monitoring of the healthcare services.

The Healthcare Effectiveness Data and Information Set (HEDIS) is a dataset tool used for the measurement of the performance of healthcare services and care. It consists of 81 measures and 5 care domains. Managed Care Organizations utilize the HEDIS data to compare their care and services performance to other health plans. HEDIS is essentially supplied by NCQA (National Committee for Quality Assurance). It is used by the health plans for measuring various health issues like the persistence of treatment by beta-blocker following myocardial ischemia, the medication use for asthma, hypertension control, comprehensive care for diabetes, screening for breast cancer, medication management for antidepressant, the status of immunizations and quit smoking advice (Webber, 2012).

HEDIS can be applied to various healthcare settings. These can be the acute settings, ambulatory care settings and clinical care settings like inpatient and outpatient (Harris et al., 2015).

There are several secondary sources of HEDIS. These can be classified as several government and organizational bodies.  NCQA collects the HEDIS data and it is collected from the Healthcare Organization Questionnaire (HOQ). HOQ collects the data from the preferred provider organizations and health plan organizations. The non-survey data is collected from the Interactive Data Submission System (IDSS). The data collected are kept under strict confidentiality in a central database for maintenance (Pugh et al., 2013). The Medicare data for HEDIS is collected by NCQA for the CMS (Centers for Medicare and Medicaid Services) and state agencies. Commercial data are collected by NCQA for the US Personnel Management offices and some of the states of the United States of America (Martino et al., 2013).

The reporting of the data by the health plans, regulators and purchasers supply the HEDIS data. The data reporting process involves the auditing by NCQA or by an approved auditing firm. The various data like claims data, provider data, membership data, etc. are reviewed after collection. The obtained data are calculated as per the determination standards of NCQA. The frequency of reporting is annual. Every year the HEDIS providers need to report the data to the NCQA (Dixon et al., 2014).

UHDDS

The collection of the HEDIS data is done through the insurance claims, medical charts and surveys. The insurance claims are for the procedures, visits to the medical office and hospitalizations. The survey is usually conducted by a survey organization approved by NCQA. Clinical measures include the hybrid or administrative data collection. Administrative data includes the electronic records for the insurance claims and various services. The hybrid method of data collection is lengthy, expensive and requires medical professionals (Westover et al., 2014).

HEDIS can be used to measure the activity of β-blockers in the case of myocardial infarction and, therefore, quality intervention can be implemented. HEDIS can also be used as a comparative scale for the measurement of the population who did and did not receive dental care. HEDIS is also important for the measurement of the childhood immunization. HEDIS is also used for the survey of the experiences of the consumers in the areas of access to care, claims processing and customer service (Szabo et al., 2015).

HEDIS data are important for the researchers who are working for the improvement of the healthcare system and are dedicated to improving the measures of HEDIS. The commercial data of HEDIS are employed for the calculation of the national performance benchmarks and statistics. They are also used for setting the standards of measures, which are included in the accreditation program of NCQA. In addition, these data have been incorporated in the quality compass, which are in turn used by the purchasers, health plans, consultants and media for the analysis of the comparative health plans.

The advantages of HEDIS have been listed below.

• HEDIS involves a rigorous selection process. This includes the assessment of scientific feasibility, importance and field-testing of the measure.
• The data of HEDIS is important for the assessment of the setting goals and the current performance.
• HEDIS is also associated with better outcomes of health and practices that are cost effective.
• The measures of HEDIS are accepted and widely known as more than 90% of the health plans in the US utilizes them.

The disadvantages of HEDIS have been listed below.

• HEDIS measures have been found to overlook some of the important aspects of the quality of healthcare.
• Improvement of the HEDIS measures by the providers of healthcare has been found to cause potential harm to the patients.
• The HEDIS development process may also be flawed.

Uniform Hospital Discharge Data Set (UHDDS) is utilized for the reporting of the data of the patients in the long term, short term and acute care hospitals. It includes the items, which are based on the definitions for providing the multiple users with consistent data.

The main purpose of UHDDS for the reporting by the hospitals for the elements of the inpatient data, which also included the outpatient settings (Andrews, 2015). It is also employed to define the set of definitions and rules for the collection of hospital data for the promotion of the comparability and uniformity of data. This enables the planning and evaluation of the initiatives of healthcare for improving the patient care effectiveness and the cost involved in the care (Bielby, 2014).

OASIS

The types of settings in which the UHDDS data can be applied are the hospitals of various settings like short term and acute, long-term and psychiatric care hospitals, agencies of home health, facilities of rehabilitation and nursing homes and other healthcare settings. All the inpatient and outpatient data from various settings are employed by the UHDDS (Werley & Devine, 2013).

The primary data for UHDDS is the hospitals of various hospitals like that of short-term, long-term, psychiatric, etc. In addition, the secondary data is collected from three different sources, which can be classified as the rehabilitation facilities, providers of home health care and retirement and nursing communities. The professionals working in medical coding and billing facilities with the recipients of Medicaid and Medicare adapt themselves at the UHDDS filing. Correct coding is essential as the reimbursement rate is affected by it (Faught, Aspevig & Spear, 2014).

The reporting procedure of UHDDS is governed by the procedure of hospital inpatient in association with the guidelines, which are payer specific. All the significant procedures should be reported by the short-term settings. The significant procedures include the surgeries that involve anesthetic risk and specialized training is required to perform that. UHDDS are reported in the form of coded data. The inpatient stay details are reported to the state and federal governments. The frequency of UHDDS reporting is annual. The reports of UHDDS are submitted every year by the hospitals and other facilities (Towers, 2013).

UHDDS is usually a collection of several data items, which has to be abstracted from the medical records of the hospital. Considerable variations are found in these medical records as the data uniformity and content are minimal. However, they are used as the primary document as the data collection is proper and relevant in these records. The hospital, which sends the record, must abstract data from them repetitively to meet the demands of the users. The data abstraction should be done record form, which is central and should be made available to the users. This abstract is, however, beneficial as it improves the data accuracy, cost effective and confidentiality problems are reduced.

One major objective of UHDDS is the promotion of data uniformity among the user organizations and institutions. The physician is considered not sufficient for the permission of uniformity. The definition of the item reduces eliminates and reduces the variation in the data items.

The number of medical records assigned by the hospital is the only number for the identification of the discharged patients. It also helps to identify the criteria of UHDDS. The items for personal identifications can be abstracted from this number.

UHDDS provides comparable data to determine the hospitals, which provide the best treatments. This helps the government to reduce the costs from the patients for whom repeated admission was not done. The reimbursement rates of different hospitals can also be compared for the medical procedures, which are similar. This helps the federal government to set up a standardized reimbursement system, which will ultimately help in standardization of the care quality. 

Outcome and Assessment Information Set (OASIS) is a set of data elements that signifies the core items required to be included in the assessment for the patient of home care. It creates the basis for the measurement of the patient outcomes for the objective of quality improvement, which is outcome based. It provides data for the use of the consumers on the home health compare. OASIS provides the information on the condition of the patient and proper reimbursement (Shang et al., 2015).

The purpose of OASIS is the improvement of performance in home health care. The data items of OASIS were developed for the purpose of measurement of patient outcomes. OASIS is the data collection tool used by the home health agencies for the reporting and collection of performance data. The OASIS data is employed for multipurpose uses like calculation of the quality reports of various types provided to the agencies of home health for guiding the performance and quality efforts of improvement (O’Connor & Davitt, 2012)

OASIS is applied in the various settings like community setting, post-acute care setting, home health setting, etc. It is used for measuring the quality in the various settings.

The data collection for OASIS takes place from two sources. The primary source is the data submitted by the agencies of home health and the secondary source is the data submitted by the Medicare claims. The OASIS data is calculated by using the entire care episode of care that commences with the admission to the agency and ends with the transfer or discharge of the patient. The claims data calculations are done based on the first claim of home health, which commences with the care episode of the patient and ends after one or two months of the opening claim (Han et al., 2013).

The reporting process of OASIS involves the collection of data from the non-Medicaid and non-medicare patients and sent to CMS. The assessment data of the patients are collected by the home health agencies and submitted to the CMS at the specified time. However, breaching of the guidelines of the reporting process will eventually result in the reduction in the reimbursement rates (Olson et al., 2014).

The OASIS data are collected at the start of patient care and thereafter at a frequency of 2 months until the discharge of the patient.

OASIS contains the data items, which were implemented for the sole intention of measuring the outcomes of the patients for the improvement of performance in the home health care. They address the environmental, socio-demographic, health status, support system, characteristics of utilization of health service and functional status of the patient. The OASIS assessments are gathered for the Medicaid and Medicare adult patients who are receiving the health services from the home health agencies. However, OASIS data are not collected for the patients who are reimbursed for their care by the payer other than Medicaid or Medicare.

OASIS is a vital component of the partnership of the industry of home care with Medicare to monitor and fosters the betterment of the outcomes of the home healthcare. It has been designed for providing the data items required for the measurement of the risk factors and outcomes of the patient. The data items are utilized for care planning, clinical assessment and other applications of agency level. The software of CMS required by the home health agencies for the submission of OASIS is Haven. The home health agencies must transmit and encode the data items using Haven as per the federal requirements. The sent data items must conform to the standards of CMS.

References

Andrews, R. M. (2015). Statewide hospital discharge data: Collection, use, limitations, and improvements. Health Services Research, 50(4), 1273-1299. doi:10.1111/1475-6773.12343

Bielby, J. A. (2014). Reliable coded data require a reliable coding process framework. J AHIMA, 85(10), 80-82.

Dixon, B. E., Gibson, P. J., Frederickson, C. K., & Rosenman, M. (2014). Measuring Population Health Using Electronic Health Records: Exploring Biases and Representativeness in a Community Health Information Exchange. Studies in health technology and informatics, 216, 1009-1009.

Faught, I. C., Aspevig, J., & Spear, R. (2014). New Means of Data Collection and Accessibility. In Public Health Informatics and Information Systems (pp. 375-398). Springer London.

Han, S. J., Kim, H. K., Storfjell, J., & Kim, M. J. (2013). Clinical outcomes and quality of life of home health care patients. Asian Nursing Research, 7(2), 53-60. doi:10.1016/j.anr.2013.03.002

Harris, A. H., Ellerbe, L., Phelps, T. E., Finney, J. W., Bowe, T., Gupta, S., ... & Trafton, J. (2015). Examining the specification validity of the HEDIS quality measures for substance use disorders. Journal of substance abuse treatment, 53, 16-21.

Martino, S. C., Weinick, R. M., Kanouse, D. E., Brown, J. A., Haviland, A. M., Goldstein, E.. . Elliott, M. N. (2013). Reporting CAHPS and HEDIS data by Race/Ethnicity for medicare beneficiaries. Health Services Research, 48(2pt1), 417-434. doi:10.1111/j.1475-6773.2012.01452.x

O'Connor, M., & Davitt, J. K. (2012). The Outcome and Assessment Information Set (OASIS): A review of validity and reliability. Home health care services quarterly, 31(4), 267-301.

Olson, C. H., Dierich, M., Adam, T., & Westra, B. L. (2014). Optimization of decision support tool using medication regimens to assess rehospitalization risks. Applied clinical informatics, 5(3), 773-788.

Pugh, M. J. V., Marcum, Z. A., Copeland, L. A., Mortensen, E. M., Zeber, J. E., Noël, P. H.. . Hanlon, J. T. (2013). The quality of quality measures: HEDIS® quality measures for medication management in the elderly and outcomes associated with new exposure. Drugs & Aging, 30(8), 645-654. doi:10.1007/s40266-013-0086-8

Shang, J., Larson, E., Liu, J., & Stone, P. (2015). Infection in home health care: Results from national Outcome and Assessment Information Set data.American journal of infection control, 43(5), 454-459.

Szabo, S. M., Osenenko, K. M., Qatami, L., Korenblat Donato, B. M., Korol, E. E., Al Madani, A. A., ... & Levy, A. R. (2015). Quality of care for patients with type 2 diabetes mellitus in Dubai: a HEDIS-like assessment. International journal of endocrinology, 2015.

Towers, A. L. (2013). Clinical Documentation Improvement—A Physician Perspective. J AHIMA, 84(7), 34-41.

Webber, C. (2012). HEDIS Audits: Addressing Challenges, Creating Opportunities (Doctoral dissertation, THE COLLEGE OF ST. SCHOLASTICA).

Werley, H. H., & Devine, E. C. (2013). of Essential Nursing Data. Nursing Informatics: Where Caring and Technology Meet, 160.

Westover, C., Arredondo, P. H., Chapa, G., Cole, E., & Campbell, C. R. (2014). Quality of care in a Lowâ€ÂIncome Consumerâ€ÂDriven health plan: Assessment of healthcare effectiveness data information set (HEDIS) scores for secondary prevention. Journal for Healthcare Quality, 36(3), 28-34. doi:10.1111/jhq.12001