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Uniform Hospital Discharge Data Set Essay

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Updated: Dec 4th, 2020

Introduction

The Uniform Hospital Discharge Data Set (UHDDS) refers to information for inpatient admissions, which is collected when patients are discharged. The data is used in the administration of Medicaid and Medicare programs and the standardization of health care. The main goal of UHDDS is to have data that can be easily compared for managerial purposes. Besides, health care providers can use it for billing purposes. The largest percentage of the information required can be collected from the admission form’s fact sheet. The UHDDS can be used by rehabilitation facilities, home health care providers, and nursing and retirement facilities.

Elements of the UHDDS

The UHDDS is comprised of several elements that simplify the collection of information. They include a hospital identification number, principal diagnosis, gender, age, race, insurance payer number or code, additional significant diagnoses, the specific episode of care, and medical procedures performed (Aalseth, 2015). The data elements can be classified into four groups, namely financial information, provider information, patient identification, and the specific episode of care (Nelson & Staggers, 2014). Personal identification is used to generate a medical record number while race and ethnicity are used to different population groups based on their unique characteristics.

For data collection purposes, the government recognizes five races, namely white, black, American Indian, Asian, and unknown (LaCour & Davis, 2016). Ethnic designations include Hispanic origin and the unknown. The patient is also required to provide their location of residence. Other elements of the UHDDS include release date, the identity of attending physician, total charges for services provided, and the patient’s disposition at discharge. The aforementioned details should be specified to provide uniform information that contains few or no errors (Nelson & Staggers, 2014). For example, a lack of data specification can affect the rate of reimbursement and the consequent evaluation of health care quality. Besides, if the information is poorly recorded, internal and external reporting burdens increase (LaCour & Davis, 2016). The inconsistencies that arise compromise the effective application of the results by different organizations.

Consequences

Some of the consequences of a lack of specification include inaccurate reporting, poorly standardized data, and inaccurate comparison of health care facilities. Precise information helps to compare the quality of services offered by various health care facilities and determine the best ones concerning the treatment of patients (McWay, 2013). The finest hospitals can then be used by the government as role models to lower health care costs. The UHDDS is also used to compare the rates of reimbursement of different hospitals. The government cannot create a standardized system of compensation if the information provided is not specified (McWay, 2013). In that regard, the prices of health care services would vary from facility to facility because of a lack of consistency. It would also present challenges that would prevent hospitals from improving the quality of care that they provide. These challenges would hinder the creation of an effective health care system. In that regard, patients should provide accurate information to facilitate the provision of equal levels of care.

Conclusion

The UHDDS allows the government and health care facilities to have comparable data that can be used to improve the delivery of health care services. In that regard, patients need to provide accurate information. Elements of the UHDDS include patient and facility identification, principal diagnosis, gender, race, and ethnicity, expected insurance payer, and procedures conducted. The UHDDS can be used by hospitals, rehabilitation facilities, and nursing communities to provide better patient care.

References

Aalseth, P. (2015). Medical coding: What it is and how it works (2nd ed.). Burlington, MA: Jones & Bartlett Learning.

LaCour, M., & Davis, N. A. (2016). Foundations of health information management. New York, NY: Elsevier Health Sciences.

McWay, D. C. (2013). Today’s health information management: An integrated approach (2nd ed.).New York, NY: Cengage Learning.

Nelson, R., & Staggers, N. (2014). Health informatics: An interprofessional approach. New York, NY: Elsevier Health Sciences.

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IvyPanda. 2020. "Uniform Hospital Discharge Data Set." December 4, 2020. https://ivypanda.com/essays/uniform-hospital-discharge-data-set/.

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IvyPanda. (2020) 'Uniform Hospital Discharge Data Set'. 4 December.

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