Here are some common health care data analytics terms to know so you can speak the same language with your team.
The amount of data and technology readily available changes how hospitals operate and how health care is delivered. Hospital leaders should have a basic understanding of the tools and technology available to them. When an organization's leaders and data experts speak the same language, they have a better chance of facilitating clinical, financial and operational improvement. Here are some of the six most common data and analytics terms that every children's hospital executive should know.
Internet of things (IoT)
IoT in health care is the growing connectivity between mobile technology and patients. It's about leveraging the internet and a patient's personal devices for an improved patient experience. By connecting patients with their health data, it allows them to have agency and access to their own information.
It also helps clinicians interact with and monitor patients to prevent hospital admissions. IoT enables growing connectivity as consumers shift to smart personal devices for themselves, their cars and homes, creating a more interconnected world.
This is a system for securely storing data and applications over the internet instead of on local servers within the organization. Many health care computer applications are moving to the cloud, including electronic health records (EHRs), talent management, financial data applications and health information exchanges.
This change eliminates the need for updates, establishes software consistency and improves cost efficiency while maintaining privacy. According to the 2016 HIMSS Analytics Cloud Survey, 73 percent of respondents say they are using or plan to use the cloud to better engage and empower their patients.
This describes the vast amounts of data that is available within or outside of an organization. With the help of big data, the health care industry can predict epidemics and outbreaks, reduce costs and improve quality of life for patients.
By collecting massive amounts of information from electronic health records (EHR), patient-generated data sources and bio specimens from thousands, even millions of patients, health care analysts can help clinicians make the best care decisions—faster.
Data management solutions
These solutions include the next generation of decision support systems that are vital to collecting the various types of information available (EHR, lab results, claims and reimbursement data) and quickly disseminating this information to those who need it. For example, CHA's Pediatric Analytic Solutions brings together financial, operational and clinical data so children's hospitals can assess improvement strategies.
Predictive analytics and AI
By leveraging information from existing datasets, identifying patterns and predicting outcomes, predictive analytics can reduce costs and improve a patient's results through preventive actions at the point of care.
Data scientists use predictive analytics to look at a variety of factors and possible outcomes to predict which patients are at the highest risk for readmission. Artificial intelligence (AI) refers to programs that use algorithms, natural language processing or machine learning to learn and improve scale and speed of data analysis over time.
Patient management software
These are platforms that share information between patients, providers, health plans and systems. For example, some patient portals allow the consumer to schedule appointments, pay bills and access lab results from a computer or a mobile device. As the health care industry begins to align business practices with consumer industries, it is challenged with providing user-friendly and HIPAA-compliant solutions.
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