As healthcare organizations evolve into technology-driven, data-rich entities, the need for comprehensive healthcare data models is growing. These models can improve the efficiency of the health system and uncover new opportunities to optimize patient outcomes.
As a data leader, you understand the importance of having reliable and up-to-date information about patient care to deliver high-quality and cost-effective medical services. By creating an enterprise framework for your health system’s data assets and designing appropriate healthcare data models, you can gain valuable insights from large datasets across multiple sources, enabling better decision-making for clinical care scenarios and population health initiatives.
This blog post will explore essential healthcare data models that can help your organization make the most of its data, optimizing its use while achieving maximum impact on patient outcomes.
How to Choose the Right Data Models for Healthcare Data Types
Consistent representation and storage of healthcare data is highly necessary. To achieve this, it is essential to pay close attention to data models, as they play a pivotal role in integrating data into healthcare platforms.
Let’s delve into the two primary healthcare data types that hospitals and health systems deal with daily, and explore the most appropriate data models to accommodate them.
Clinical Data
Clinical data is an essential asset for healthcare providers as it provides valuable insights into patient safety and care programs. At its core, clinical data assists in making decisions based on a patient’s unique medical history, current health status, and risk factors. However, with the aid of advanced analytics and regulatory standards, it can go beyond that. It can reveal latent patterns in the data that identify opportunities for improved services or strategies for addressing public health issues. In addition, it can provide feedback to healthcare professionals about their practice changes and outcomes over time, leading to better results for both providers and patients.
Claim Data
Claims data contains important information on medical procedures, treatments, and services that healthcare providers bill to insurance companies, making it a crucial type of healthcare data. This data is typically collected by healthcare providers and then submitted to insurance companies for reimbursement. Insurance providers frequently utilize claims data to determine reimbursement rates, detect fraud, and monitor the quality of care delivered by healthcare providers.
Claims data is a valuable resource for healthcare providers as well. By analyzing this data, healthcare providers can identify patterns and trends in the type of care they provide, as well as areas where they can improve their services. For example, if claims data shows that they are using a particular type of treatment more frequently than others, healthcare providers can investigate why that might be the case and whether there are alternative treatments that might be more effective or cost-efficient.
Claims data can also be used to identify gaps in care. For example, if claims data shows that patients with a particular condition are not receiving regular check-ups or screenings, healthcare providers can take steps to ensure that these patients receive the necessary care. Moreover, healthcare providers can use claims data to identify patients who may be at risk for certain conditions, enabling them to intervene early and prevent the development of more serious health problems.
One of the challenges of working with claims data is ensuring that the data is accurate and up-to-date. In order to ensure timely submission of claims and accurate information entry, healthcare providers and insurance companies should work together. Additionally, it is important to ensure that patient privacy is protected when working with claims data. This means that only authorized personnel should have access to the data, and appropriate measures should be in place to prevent data breaches.
The APCD model
The All-Payer Claims Database (APCD) model is a healthcare data model that aims to improve healthcare outcomes by gathering comprehensive claims data from all payers, including public and private insurance companies. The APCD model collects data on healthcare services and procedures, their costs, and patient demographics, among other relevant information.
The APCD model is particularly useful in addressing healthcare spending and cost issues. It does so by allowing policymakers and healthcare providers to analyze and compare data across various payers. It also helps them to identify areas where cost reduction is possible. By analyzing the data provided by the APCD model, stakeholders can identify areas of overspending or inefficiencies, leading to more informed decisions about resource allocation and cost management.
Another significant benefit of the APCD model is its ability to improve the quality of care provided to patients. By analyzing the data, healthcare providers can identify best practices, patient needs, and potential gaps in care. This information can then be used to improve care delivery, resulting in better patient outcomes.
The APCD model also helps in identifying patterns and trends in healthcare service delivery and utilization. This feature can be used to inform public health policies and initiatives. For instance, the data can be analyzed to identify the prevalence of certain conditions and populations that are at higher risk. It can also identify the areas that lack adequate healthcare services.
The APCD model is a valuable tool for healthcare stakeholders looking to improve healthcare outcomes and manage costs. By gathering comprehensive claims data from all payers and using advanced analytics to analyze the data, the APCD model provides insights into healthcare spending and quality of care. Hence, leads to better resource allocation and decision-making.
Comparison of the three Healthcare Data Models
| Model Name | Description | Data Types | Benefits |
| Clinical Data Model | Stores health information in digital formats such as lab results, patient data, medical histories, immunization dates, and virtually any other type of medical data imaginable | Patient data, medical histories, lab results, immunization dates, etc. | Provides important insights into patient safety and care programs. Helps uncover latent patterns in the data that reveal opportunities for improved services. Provides feedback to individual healthcare professionals about their practice. Changes and outcomes over time, and leads to better outcomes for providers and their patients. |
| Claims Data Model | Stores data generated during the billing process for healthcare services provided to patients | Diagnosis codes, procedure codes, provider information, and payment information | Provides a comprehensive view of healthcare utilization patterns. Allows healthcare providers to identify inefficiencies and opportunities for cost savings. Also, supports population health management initiatives. |
| APCD Model | Collects data from multiple sources to provide a comprehensive view of healthcare utilization and cost data | Claims data, pharmacy data, eligibility data, provider data, and clinical data | Provides detailed insights into healthcare utilization and cost, supports data-driven decision making for population health initiatives. Enables the identification of high-risk patients, and supports the development of effective healthcare policies. |
Each of these healthcare data models has its own unique benefits. Also each can be used to gain insights into different aspects of healthcare data. By utilizing these models, healthcare organizations can improve patient outcomes, reduce costs, and optimize their operations.
The FHIR® Standard
To store digital clinical data securely, healthcare providers rely on the FHIR® standard. FHIR® stands for Fast Healthcare Interoperability Resources and is an open-source database. It can store a range of health information in digital formats. This information includes lab results, medical histories, immunization dates, and virtually any other type of medical data. This secure database format allows for the development of applications that can easily interact and share crucial patient insights from various digital channels. Moreover, FHIR® protocol has added security features that regulate access to Protected Health Information (PHI). With that, the database also ensures patient data privacy while enabling seamless sharing of vital healthcare information between doctors or institutions.
The selection of a specific healthcare data model is dependent on the intended use case. If you aim to utilize clinical data for research purposes, there are several data models worth considering.
The Informatics for Integrating Biology and the Bedside Model (i2b2)
The Informatics for Integrating Biology and the Bedside (i2b2) model is an open-source software platform. It helps support research and analysis of clinical data. It enables researchers to conduct advanced analytics and data mining to identify patient cohorts, analyze clinical and research data. Also, it is beneficial with hypothesis generation and testing. The i2b2 model is gaining popularity as a necessary tool in the healthcare industry. Because it can efficiently and effectively provide clinicians with the necessary data. Hence allow them to improve patient care and outcomes.
One of the most significant benefits of the i2b2 is that it enables the integration of data from multiple sources. This includes electronic health records (EHRs), billing data, and other clinical systems. The platform enables researchers to create queries to extract specific data from these various sources. Also, allows them to quickly and efficiently analyze and interpret data.
Another important feature of the i2b2 model is its ability to provide real-time access to data. Clinicians can access patient data quickly and easily, which can improve decision-making and ultimately lead to better patient outcomes. This feature is particularly important in critical care situations where time is of the essence. So, quick access to patient information can make a significant difference in the course of treatment.
The i2b2 model also provides clinicians with the ability to generate reports and visualizations of patient data. It makes data easier to interpret and understand. This feature is particularly useful for clinicians who may not have advanced data analytics skills but still need to understand the data to make informed decisions. The visualizations and reports generated by the i2b2 model can help identify trends and patterns in patient data. Hence, leading to better decision-making and ultimately improving patient outcomes.
Observational Medical Outcomes Partnership (OMOP)
OMOP (Observational Medical Outcomes Partnership) is a data model that supports the analysis of observational healthcare data. It was developed by a public-private partnership in the United States. The goal was to create a common data model that would allow for the integration and analysis of data from various sources, such as electronic health records, claims data, and registries.
The OMOP model consists of a standardized set of tables and fields that are used to represent healthcare data. These tables cover a wide range of topics, including patients, healthcare encounters, diagnoses, procedures, medications, and more. The use of a common data model allows for data to be easily integrated from different sources, and enables researchers to conduct analyses across large and diverse datasets.
The utilization of standardized terminologies and vocabularies in the OMOP model ensures that it can map data from different sources to a common set of concepts, providing a key advantage. This particular feature allows for more accurate and consistent analysis. The OMOP model also includes standardized code sets for diagnoses, procedures, and medications. This can eventually help to reduce variability in the way that these concepts are recorded and reported.
Another important feature of the OMOP model is its support for longitudinal analyses. Because the model captures data over time, it is possible to track changes in patient health status and outcomes over time, and to identify trends and patterns in healthcare utilization and outcomes. This can be particularly valuable for studying chronic diseases and other long-term health conditions.
The OMOP model is a valuable tool for analyzing observational healthcare data. Its use of standardized terminologies and code sets, support for longitudinal analyses, and ability to integrate data from multiple sources make it a powerful resource for researchers and clinicians alike.
Conclusion
Each of these healthcare data models has unique benefits and can be used to gain insights into different aspects of healthcare data. By utilizing these models, healthcare organizations can improve patient outcomes, reduce costs, and optimize their operations. You can choose the one on the basis of the specific needs of your healthcare system.
