Closing the loop. Practice management software (PMS); Electronic health record (EHR); hospital information system (HIS); electronic prescribing (eRX); personal health record (PHR); health reimbursement account (HRA) http://www3.phytel.com/solutions/solutions.aspx As people and processes are increasingly moving online, there has been an explosion of big data. With this on demand, virtually space-unlimited platform called the internet, every little seemingly inconsequential piece of data can be collected and thrown into a database. But the agglomeration all of that data is just the first step. The next question is how to effectively use that data to achieve some purpose. In the modern ad tech industry, it means monitoring and tracking user online behavior, then using machine learning algorithms overlaid with this behavioral data to create a data-rich profile for each unique user. Goodbye catalogs – this ad tech operates in real time to deliver individualized ads at scale. Big data is also transforming some health care operations. Million-patient internet databases are changing disease prevention, diagnosis and treatment, as well as care provider efficiency. Click here to see ten examples of how big data is used in various industries.
A prime example of a big data healthcare is Phytel, a Dallas based company that provides patient population management and engagement tools. Phytel is doing two very important things:
Phytel is helping to close that loop. The Phytel products provide not only tools to understand a given population’s health, but also supply the tools to engage and communicate with the target demographics. How does Phytel do this? One of the products, Coordinate, scans across the patient database and establishes cohorts by intelligently grouping patients according to specified conditions and clinical protocols. Care providers can search for particular groups with customizable filters. Coordinate identifies patient cohorts that could benefit from a specified action. For example, a care provider could search the database for high-risk patients suffering from depression. Coordinate would return high risk groups to the care provider, probably grouped according to range of risk factors (such as missed appointments or unfilled prescription).
A prime example of a big data healthcare is Phytel, a Dallas based company that provides patient population management and engagement tools. Phytel is doing two very important things:
- making patient management more effective for the health care provider
- motivating patients to take control of their own healthcare and treatment.
Phytel is helping to close that loop. The Phytel products provide not only tools to understand a given population’s health, but also supply the tools to engage and communicate with the target demographics. How does Phytel do this? One of the products, Coordinate, scans across the patient database and establishes cohorts by intelligently grouping patients according to specified conditions and clinical protocols. Care providers can search for particular groups with customizable filters. Coordinate identifies patient cohorts that could benefit from a specified action. For example, a care provider could search the database for high-risk patients suffering from depression. Coordinate would return high risk groups to the care provider, probably grouped according to range of risk factors (such as missed appointments or unfilled prescription).
Demonstrate Results http://www3.phytel.com/solutions/population-health-management-systems/population-health-management-engage.aspx Now here is where the loop is really brought back to the patient; Phytel’s Outreach executes targeted, personalized responses to individuals based on the results from the care-provider directed intelligent search. Through messages like voice, email, or text, Outreach will encourage the patient to take a desired action to improve his or her health. A few examples of patient outreach goals: callD care manager, depression screening, medication reconciliation, diet and exercise education series. Phytel’s arsenal follows up on each patient’s actions, creating visibility and accountability as recommended action items are documented. Below, the goal is to improve the patient's high-density lipoprotein (HDL) levels. You can see the goal details, tasks, and barriers, and also visualize the completeness of the action items.
In addition, Phytel's Remind prompts patients about their upcoming appointments, while Transition monitors every discharged patient, detects high risk factor patients, and notifies the care provider that a patient needs immediate assistance or intervention.
I like seeing companies utilizing big data to improve healthcare, and according to KLAS, a global research firm, Phytel is the best in this business. A December report issued by KLAS ranked Phytel at the top of the 29 health management vendors KLAS studied. A whopping 100% of Phytel clients interviewed reported tangible benefits after implementation of Phytel, and an impressive 89% said Phytel was the IT solution most valuable to managing their population health and strategies. Patient engagement and care management were also ranked highly in the surveyed clients.
Crunchbase reports that Phytel was started in 1996 and has seven employees, although that may change after the recent acquisition by IBM. It looks like the company had $22.5 million in funding from three rounds of investing by one investor, Polaris Partners.
It's easy to get disillusioned and bitter about the influx of data in our daily lives, but the emergence of big data in healthcare is much less a blight than a powerful, effective tool for improving the health of our society. Not only does big data improve the financial and operational efficiency of care centers, but it also improves the quality of care of the individual. Big data means that all of the pieces of data for a particular patient, gathered from all of the various sources, can be combined and compared against populations. What this means is a patient's information is no longer analyzed in isolation; the lessons that have emerged from a large group of similar patients can be used to help the individual by identifying risk factors, preventing diseases, minimizing rehospitalization events, and tailoring most effective treatments. These outcomes have the potential to be greatly improved because big data can reveal highly specific patterns, thus giving mechanistic insight into a patient's particular health.
In addition, Phytel's Remind prompts patients about their upcoming appointments, while Transition monitors every discharged patient, detects high risk factor patients, and notifies the care provider that a patient needs immediate assistance or intervention.
I like seeing companies utilizing big data to improve healthcare, and according to KLAS, a global research firm, Phytel is the best in this business. A December report issued by KLAS ranked Phytel at the top of the 29 health management vendors KLAS studied. A whopping 100% of Phytel clients interviewed reported tangible benefits after implementation of Phytel, and an impressive 89% said Phytel was the IT solution most valuable to managing their population health and strategies. Patient engagement and care management were also ranked highly in the surveyed clients.
Crunchbase reports that Phytel was started in 1996 and has seven employees, although that may change after the recent acquisition by IBM. It looks like the company had $22.5 million in funding from three rounds of investing by one investor, Polaris Partners.
It's easy to get disillusioned and bitter about the influx of data in our daily lives, but the emergence of big data in healthcare is much less a blight than a powerful, effective tool for improving the health of our society. Not only does big data improve the financial and operational efficiency of care centers, but it also improves the quality of care of the individual. Big data means that all of the pieces of data for a particular patient, gathered from all of the various sources, can be combined and compared against populations. What this means is a patient's information is no longer analyzed in isolation; the lessons that have emerged from a large group of similar patients can be used to help the individual by identifying risk factors, preventing diseases, minimizing rehospitalization events, and tailoring most effective treatments. These outcomes have the potential to be greatly improved because big data can reveal highly specific patterns, thus giving mechanistic insight into a patient's particular health.
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