Claire Dawson
Hospital data can be simplified for SAS using data templates. These templates are well-defined data structures that contain data descriptors but no data. They help to streamline SAS programming by separating voluminous data descriptions into modules, making data processing modules leaner and more concise. This, in turn, simplifies data preparation and makes it easier to develop, debug, understand and work with the data. SAS Health Solutions also offer faster data integration, effective model development, and reduced cloud costs. SAS Viya, for example, offers a private trial environment for users to experience these capabilities first-hand.
| Characteristics | Values |
|---|---|
| SAS data template | A well-defined data structure containing a data descriptor but no data |
| SAS data template benefit | Unclutter voluminous data descriptions by separating them into modules apart from the rest of the SAS code |
| SAS Health Solutions | Faster data integration, effective model development, and reduced cloud costs |
| SAS Health Analytics Framework | Improves patient diagnoses, tailors patient pathways, accelerates clinical research, prevents diseases, and connects social determinants of health |
| SAS Health Analytics Framework | Helps hospitals, health systems, researchers, and patients |
Explore related products
$38.28 $48.99
What You'll Learn
Using SAS data templates
The main benefit of using SAS data templates is that it allows you to unclutter voluminous data descriptions by separating them into modules apart from the rest of your SAS code. This makes your data processing modules lean and concise, and therefore easier to develop, debug, understand and work with.
When you construct SAS data tables using SAS code or data management tools such as SAS Data Integration Studio, data descriptions can overshadow the rest of your more sophisticated data processing logic. With tens, and sometimes hundreds, of variables to describe, your data preparation code or process can become unsightly, tedious and bulky.
One way to apply your data template to a newly created dataset is to:
- Copy your data template in that new dataset
- Append your data table to that new data set
For example, your variable types and lengths should be the same on the BASE= and DATA= tables; labels, formats and informats will be carried over from the BASE= dataset/template so you can skip assigning them on the DATA= table.
Vaccination Rules: Hospital Workers and Their Rights
You may want to see also
Explore related products
Streamlining ETL tasks
Utilize Data Templates
SAS data templates are powerful tools for streamlining ETL processes. A data template is a well-defined data structure containing a data descriptor and zero observations. By using data templates, you can separate voluminous data descriptions from your data processing modules. This approach helps to unclutter and simplify your SAS programming. For instance, when creating a new dataset, you can copy your data template, append your data table, and benefit from consistent variable types and lengths across tables. This reduces the need for repetitive coding and streamlines the data preparation process.
Adopt Industry-Standard Data Formats
To streamline data integration, it is beneficial to use industry-standard data formats and sources, such as FHIR. This enables seamless data ingestion and mapping to common data models, facilitating interoperability and efficient data exchange between different systems and applications. Standardizing data formats reduces the complexity associated with data transformation and ETL tasks.
Leverage Cloud-Based Analytics
Moving analytics to the cloud, such as with SAS and Microsoft Azure, offers significant advantages for streamlining ETL tasks. Cloud-based infrastructure improves efficiency by reducing latency and processing time. It also provides a flexible environment to access new analytic capabilities and expand the use of data-driven insights. With cloud-based solutions, you can unify insight generation and dissemination, making data and analytics more accessible to stakeholders across clinical and operational teams.
Automate Data Processes
Automation plays a pivotal role in streamlining ETL tasks. Solutions like SAS Health offer automation capabilities that simplify health data management and accelerate analytic discovery. By automating data processes, you can improve efficiency, reduce manual interventions, and enable seamless data flow across the healthcare ecosystem. Automation also enhances interoperability and makes it easier to ingest and integrate data from various sources.
Enhance Data Integration with SAS Viya
SAS Viya is a powerful tool that facilitates faster data integration and effective model development while reducing cloud costs. By leveraging SAS Viya, organizations can benefit from a single, expandable environment that streamlines the ETL process. It empowers users to transform data into actionable intelligence with its extensible, sensor-based data model and intuitive visual interface. SAS Viya's private trial environment allows users to experience these capabilities firsthand.
Hospital Code 10-13: Understanding the Meaning of This Emergency
You may want to see also
Explore related products
Health data management
Over the years, health data management systems have evolved from paper-based records to computer-based systems, and now leverage cloud computing, the Internet of Things (IoT), and blockchain technology. This evolution has brought new challenges, such as data aggregation, preprocessing, security, and privacy concerns due to the increasing number of data breaches and hacking incidents.
One of the primary goals of health data management is to create a comprehensive view of patients, households, and patient groups. By integrating various data sources, such as EMR abstracts, claims data, enrollment information, and medical program data, health data management systems can generate composite profiles. These profiles enable predictions, proactive measures, and targeted patient engagement, ultimately improving patient health outcomes.
To address the challenges of data fragmentation and duplication, health data management systems aim for real-time data access and integration. This ensures that data is up-to-date and consistent across healthcare providers, public health organizations, insurance companies, pharmacies, and patients themselves. Additionally, patient identity privacy and data security are crucial considerations to protect sensitive patient information.
SAS, a software company, offers tools like SAS Health Episode Builder and SAS data templates to simplify health data management. SAS Health Episode Builder helps analytic leaders understand cost drivers, comorbidities, and expected outcomes. SAS data templates, on the other hand, provide a well-defined data structure with necessary attributes, allowing users to separate data descriptions from data processing logic, resulting in more concise and manageable code.
United Healthcare: Froedtert Hospital's Insurance Acceptance
You may want to see also
Explore related products
Data-driven insights
SAS Health Analytics Framework offers an end-to-end solution for hospitals to simplify health data management and accelerate analytic discovery. With built-in models, hospitals can understand population-level cost drivers, comorbidities, and expected outcomes for specific episodes of care. This enables clinicians to optimise treatment pathways and make informed decisions about patient care. Additionally, financial analysis supported by these analytics assists risk management professionals in their important work.
Italy's second-largest hospital, for example, utilised advanced analytics for an effective pandemic response. This showcases how analytics can play a pivotal role in helping hospitals tackle unforeseen challenges and improve their overall resilience. Furthermore, advanced analytics in the cloud has helped an international biopharmaceutical group to enhance its operations and efficiency, demonstrating the far-reaching benefits of data-driven insights.
Through data-driven insights, hospitals can also improve their operational processes. By deploying a low-code/no-code environment, hospitals can explore data, develop advanced analytics, and deploy models to gain insights that improve efficiency and enhance the value of analytics. AI-driven predictive analytics can be used to predict patient demand and optimise operations, making each patient interaction more relevant and proactive.
Additionally, data-driven insights can be used to monitor medication adherence based on various factors, such as condition, region, drug tier, age, and compliance rate. This enables hospitals to provide tailored interventions and improve patient outcomes. By utilising data effectively, hospitals can improve patient satisfaction, enhance the clinician experience, and ultimately, save more lives.
Hand Contamination Measurement: Hospital Strategies for Reducing Germs
You may want to see also
Explore related products
Data preparation code
SAS data templates are a powerful tool for simplifying hospital data preparation. They are well-defined data structures that contain a data descriptor and all the necessary attributes (variable types, labels, lengths, formats, and informats), but no actual data. This allows you to separate voluminous data descriptions from your data processing modules, making your code leaner, more concise, and easier to develop, debug, understand, and work with.
To use a data template, you can copy it into a new dataset and then append your data table to this new dataset. This ensures consistency in variable types and lengths between the BASE= and DATA= tables, simplifying your code. For example, labels, formats, and informats will be carried over from the BASE= dataset, so you don't need to assign them on the DATA= table.
SAS also offers various solutions to simplify health data integration, management, analytics, and storage, such as SAS Health Solutions and SAS Viya. These solutions enable users to make data-driven decisions, improve patient diagnoses, accelerate clinical research, and more. For instance, Italy's second-largest hospital used advanced analytics for an effective pandemic response.
Additionally, SAS Health Analytics Framework helps analytic leaders understand population-level cost drivers, comorbidities, and expected outcomes for specific episodes of care. It also includes financial analysis tools for risk management professionals and clinicians optimizing treatment pathways. By using industry-standard formats like FHIR, SAS simplifies health data management and accelerates analytic discovery.
SAS and Microsoft Azure, together, drive innovation and improve patient outcomes by addressing data barriers. They help hospitals, health systems, researchers, and patients by improving interoperability and flexibility within a secure cloud environment. This collaboration enhances clinical and operational decision support and accelerates research to improve lives.
Healing a Broken Thumb: Hospital Treatment Options
You may want to see also
Frequently asked questions
A SAS data template is a well-defined data structure containing a data descriptor but no data. It helps to unclutter voluminous data descriptions by separating them into modules, making your data processing modules leaner and concise.
Copy your data template into a new dataset and append your data table to this new dataset. This will ensure that variable types and lengths are the same, and labels, formats, and informats will be carried over.
SAS simplifies health data management and accelerates analytic discovery, providing valuable insights on the quality and cost of care. It also helps to improve patient diagnoses, tailor patient pathways, and accelerate clinical research.
