What type of database is most commonly used in healthcare?

The digitization of the healthcare industry is occurring pretty fast, and it has changed the way healthcare data is processed. While this healthcare data has considerable potential to improve patient outcomes and improve the quality of life in general; management and security of high volumes of healthcare data can be quite challenging. 

This post examines both the strengths and weaknesses of healthcare databases and possible solutions to overcome the weaknesses.

Healthcare Database basics

Healthcare databases have become an important part of running healthcare operations today, including – patient information, labs, billing, finances, tracking, payments, etc. All the information that a medical practitioner collects from a patient becomes a part of their healthcare database. A healthcare database serves to replace the file, folders, and paper documents, making the data more convenient and accessible.

Healthcare databases are used as the base for running several transactional applications, which include the following:

EHRs, lab systems, administration, financial systems, patient identification, patient satisfaction systems, ADT tracking, billing and payment processing, HR, education, and research.

Almost every application used at a hospital or clinic runs on an OLTP database.

What is OLTP Database in healthcare?

When we talk of healthcare databases, they can be of several types, but the most commonly used database in healthcare is the OLTP (online transaction processing) database.

An example of such an application is – electronic health records (EHR). 

OLTP database allows for fast and real-time transactional processing. Let’s say, if a patient goes to a hospital or clinic, by searching for his/her name in the EHR, the front desk staff can instantly see a result. They can gain instant access to their previous treatment record, medical histories, etc. 

With OLTP, you get speedy results.

Strengths and Weaknesses of OLTP Healthcare Databases

OLTP databases enable sharing of patient data across multiple healthcare environments, but they have their own strengths and weaknesses, which we have listed below:

Strengths of OLTP Databases

• They enable standardization of business and clinical processes.
• With their help, data can be stored externally and backed up in a secure place to prevent data loss. 
• They enable quicker processing of transactions such as lab results, payment claims, etc.
• They reduce the risk of errors and misinterpretations that can negatively impact the quality of patient care.
• They enable the storage of vast amounts of data that can be used to inform better, more cost-effective care.

Weaknesses of OLTP Databases

• With OLTP databases, an overwhelming amount of raw data is created. This raw data needs to be changed into actionable and targeted knowledge because making sense of the raw data on its own can be overwhelming.
• Data siloes are a big weakness of the OLTP database in which each application has its own database. OLTP databases do not allow analytics that spans each database silo. 
• These databases are vulnerable to cyberattacks, which means sensitive patient data could fall into the wrong hands.

The solution for the above challenges:

A solution to the above weaknesses exists in the form of an OLAP (online analytical processing) database, which exists as a layer on top of another database – usually an OLTP database. OLAP database allows organizations to perform sophisticated analysis on data from a variety of sources. It can quickly respond to multi-dimensional analytical queries and makes it easy to analyze data from multiple perspectives.

Extracting actionable insights from large data sets is a great benefit that OLAP brings to the table, which helps healthcare organizations in several ways, such as – efficient reporting, quality control of services, fast, interactive querying, simplified decision making, better tracking of medical parameters, etc.

Hospital data are available from a myriad of sources, including individual hospitals and hospital associations, State and regional data organizations, health planning or health data organizations at the state level, departments of health, and Federal agencies. Many hospital quality measures are created using hospital administrative discharge data. These data sets provide information on:

• Patient demographics.
• Diagnoses.
• Procedures.
• Admission source.
• Discharge status.
• Length of stay.
• Charges.

Despite certain limitations (lack of clinical detail, coding variations, time lags), quality experts regard administrative data as a reliable and usable source for the purpose of assessing hospital quality.

To calculate hospital-specific results and benchmarks, you can access two hospital quality databases: Healthcare Cost and Utilization Project (HCUP) and Medicare Provider Analysis and Review (MEDPAR).

Healthcare Cost and Utilization Project (HCUP)

HCUP is a family of health care databases and tools sponsored by the Agency for Healthcare Research and Quality (AHRQ). HCUP databases, which contain data elements from inpatient and outpatient discharge records, bring together the data collection efforts of State data organizations, hospital associations, private data organizations, and the Federal Government to create a national information resource of patient-level health care data. HCUP includes the largest collection of longitudinal hospital care data in the United States, with all-payer, encounter-level information beginning in 1988. HCUPnet, an on-line interactive query tool, allows access to data without purchase (http://hcupnet.ahrq.gov/).

Medicare Provider Analysis and Review (MEDPAR)

This data file, available from the Centers for Medicare & Medicaid Services (CMS), contains records on all Medicare beneficiaries who use hospital inpatient services. MEDPAR contains data from claims for services provided to beneficiaries admitted to Medicare-certified inpatient hospitals and skilled nursing facilities, which are identified in the database. Report card sponsors can use these data to calculate measures relating to hospital quality, conditions, and procedures. MEDPAR data sets constitute primarily an older section of the population, which tends to consume health care services more often than the population as a whole.

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