Mediaboss Marketing

MUNCIE, Ind., Feb. 23, 2021 /PRNewswire/ --Ontario Systems, a leading provider of enterprise software that automates complex workflows, accelerates revenue recovery and simplifies the payment process for healthcare, accounts receivable management (ARM) and government clients, today announced its acquisition of Pairity, a cutting-edge provider of artificial intelligence (AI) and machine-learning capabilities that allow collection teams to maximize revenue recovery by uncovering new data insights at scale.

Pairity's advanced AI technology extracts and surfaces actionable patterns, allowing collectors to continually adapt their contact strategies. With this integrated functionality, Ontario Systems' collection platforms will help clients significantly enhance productivity and collection results.

"Pairity shares Ontario's commitment to creating intelligent workflow solutions that streamline the collections process and accelerate payments," said Ontario Systems CEO Tim O'Brien. "Pairity's technology strengthens our ability to drive value for our clients and provides another foundation from which we can continue to innovate."

Recognized as the most innovative product at the 2019 CollectTech conference, Pairity allows users to continuously identify accounts with the highest probability of successful collection.Collectors in turn require fewer phone calls to realize value, increasing efficiency and revenue.

"We greatly look forward to joining Ontario," said Greg Allen, CEO of Pairity. "Their proven track record of success in delivering enterprise workflow, collection, and payment solutions is the perfect platform on which to expand the reach of Pairity's innovative approach to collections."

This acquisition follows Ontario Systems' acquisition of SwervePay in May 2020 as part of Ontario Systems' growth and SaaS-transformation strategies designed to deliver faster innovation and increasing business value to thousands of clients nationwide.

About Ontario Systems

Ontario Systems is a premier provider of enterprise technologies that streamline and accelerate revenue recovery for clients in the healthcare, government, and accounts receivable management (ARM) markets. Through process automation and modern communication and payment tools, Ontario Systems helps its clients generate more revenue at reduced cost and engage patients, constituents, and consumers compliantly and effectively.

With offices in Indiana, Massachusetts, New Mexico, and Washington state and employees across the country, Ontario Systems helps 600+ hospital networksincluding 5 of the 15 largest systems in the U.S.optimize cash collections and provide a seamless patient financial experience. Ontario Systems also serves 8 of the 10 largest ARM companies in addition to state and municipal governments nationwide.

About Pairity

Founded on the belief that advanced technology could more effectively address consumer debt for all stakeholders, Pairity offers leading artificial intelligence and machine-learning solutions that assist 40+ companies to manage over $40B of debt more effectively. Pairity's solutions shed light into their over 10 million unique consumers by learning, organizing, and scoring behavior that drives workflow strategy more efficiently. Pairity reduces friction in the collections process by harnessing their intelligence to boost productivity and revenue generating activities.

To learn more about Ontario Systems, visit http://www.ontariosystems.com

PRESS CONTACTDaniel Ward Vice President, Marketing 765-751-7469 [emailprotected]

SOURCE Ontario Systems

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Ontario Systems Acquires Pairity to Embed the Power of Machine Learning in Its Industry-Leading Collections Technology - PRNewswire

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2021-2026 Machine Learning As A Service Market: Analysis by Business Growth, Development Factors, Applications, and Future Prospects The Bisouv...

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Key players in global Machine Learning in OrthopedicsRSIP Vision, OM1s Chief Technology, Medicrea, Sparta Science, Spentys, myrecovery.ai, ImageBiopsy Lab, Articulate Labs, AlgoSurg Inc., OrthoFeed

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Machine Learning in Orthopedics Market Research Forecasts 2021-2028 by Type, Application and Top Key Vendors KSU | The Sentinel Newspaper - KSU | The...

Machine learning seems to be picking up steam as one of the buzzwords to look out for this decade.

Among the U.S. and Japan-based I.T. professionals surveyed in 2017, three-fourths said they were already using machine learning for cybersecurity. Most were also confident that the cyberattacks on their businesses within the past year used machine learning. Despite its increasing use, machine learning remains an ambiguous concept among more than half of the respondents.

Regardless, data has become the new black gold in recent years, according to some experts. The entrepreneur in this data-driven economy relies on information derived from collected data to make more informed decisions. It wouldnt be surprising for a business to invest heavily in software and other solutions built on sophisticated neural networks.

Creating such networks is no easy task. Whether feed-forward or recurrent, a neural network must be capable of learning as it feeds on more data. It also has to learn new things in a period measured in days, if not seconds. By contrast, the human brain takes years for something to become second nature to a person.

Central to this effort is the machine learning engineer. It has grown to become the most in-demand profession in the U.S., with related job opportunities spiking by 344% in 2019. Heres an in-depth look into the role of a machine learning engineer and the reasons for the jobs increase in demand.

To say that a machine learning engineers job is similar to a computer programmer is a dichotomy. While performing programming to an extent, a machine learning engineers task is to develop the machine to perform tasks without being explicitly told.

Computer programming takes rules and data, and then turning them into solutions. Meanwhile, machine learning takes solutions and data, and then turning them into rules. Furthermore, computer programming can develop a general-use calculator, while machine learning can develop one for a specific niche.

Machine learning engineers work closely with data scientists and software engineers. They create control models using data that are derived from the models defined by data scientists, allowing the machine to understand commands. From there, the software engineer designs the user interface from which the machine will operate.

The final product is software, like cnvrg MLOps, combining best practices from DevOps, software development and I.T. operations, and machine learning engineering. Organizations tend to spend more on infrastructure development when a machine learning-ready software can provide a precise estimate on how much they need.

Machine learning engineers have a diverse skill setwith some skills encompassing those found in data scientists and software engineers. Its usual for one to graduate from college and begin working with some skills missing since theyll learn these skills as they move up the career ladder anyway.

The necessary skills for machine learning engineering fall under any of the four categories.

As mentioned earlier, the end product of machine learning engineering is software. Still, its applications are far and widebeyond predicting business trends and auto-filling search terms.

For instance, Stanford Universitys Autonomous Helicopter Program demonstrates the feasibility of teaching an aircraft flight. Researchers installed a system that uses reinforcement learning on a Yamaha R-50 helicopter. It managed to perform stunts a human-crewed helicopter would have difficulty doing, if not impossible to do, continually correcting its course with each pass.

Similar autonomous technology found its way in the drivers seat of Googles self-driving vehicle. Described as on the bleeding edge of artificial intelligence research, the car learns from human behavior on the road to drive. While the technology wont replace human drivers anytime soon, it shows the possibilities machine learning engineering is turning into reality.

Its safe to say that machine learning engineers fill capability gaps among software engineers and data scientists. When these disciplines work together, they create technologies previously thought impractical or impossible. No doubt that theyre paving the way to the future.

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What Is the Role of a Machine Learning Engineer? - TechSpective

Regional Cancer Care Associates, based in New Jersey, has more than 20 locations throughout New Jersey, Connecticut, Maryland, Pennsylvania and the Washingtonarea. Staff realized they needed a risk-stratified list of patients for COVID-19 vulnerability that nurses could manage through phone calls and by coordinatingservices with other providers.

THE PROBLEM

Because of staffing challenges, the list had to identify only the high-risk patients who staff needed to manage first, not the entire population or those patients who could wait a bit longer for nurse outreach.

"Even though we already had an indigenous and independent scoring logic/mechanism for patient risk, this was mainly based on a combination of comorbidities that differentiated it from the usual scoring techniques," explained Lani M. Alison, vice president of quality and value transformation at RCCA.

"Thus," she said, "there was a need to further stratify the risk patients for COVID-19 vulnerability and to establish a patient-centered assessment and outreach."

On another note, staff observed challenges in assigning these patients and a defined patient roster to care coordination executives or support staff, which was hindering a patient-centric outreach approach, Alison added.

PROPOSAL

RCCA turned to artificial intelligence-based health IT vendor Health EC to help address the challenges.

"HealthEC was able to run their machine learning algorithms to identify the patients at highest risk for COVID-19 and therefore focus our care coordination resources," Alison said. "Algorithms re-stratified these patients and assigned a ranking to each patient with an associated risk score."

Lani M. Alison, Regional Cancer Care Associates

The result was a defined patient list that enabled the RCCA team to reach the highest of the high-risk population. The list proved very helpful, and it became an essential part of RCCA's care management documentation platform. It helped focus initial care management calls and increase the effectiveness of the team.

"RCCA also used the list to streamline the COVID-19 huddles and provide this information to practice administrators at each of our sites to help manage patient outreach, mitigate the risk and provide educational information," she said.

MEETING THE CHALLENGE

Data was aggregated from claims, clinical, labs and HIE data sources into the universal data warehouse used by HealthEC. This created a longitudinal, 360-degree view of the patient.

"This single longitudinal view gave us easy access to all the patients' care records and pooled data, including demographics, vitals, diagnosis, etc., from different sources, like the EHR, claim files, CCDAs and ADTs," Alison explained.

"Users were able to have access to patient clinical information without jumping around into different modules. It created a one-stop shop."

HealthEC's Care Connect Pro empowered RCCA staff to stratifyhigh-risk patients (10% of its entire population), not only for COVID-19 risk management, but also for better care management overall, she said.

"Care coordinators, nurses and staff used the CCPro tool to document patient outreach, education material and medication management," she said. "Each patient was assigned a dedicated care coordinator to help mitigate the risk of hospitalization."

Along with the aforementioned clinical data, diagnostic information was added for integrated patient care plans with LabCorp data. This ensured a real-time dynamic flow of information that proved crucial for physicians to design a care pathway or to decide the next milestones of a care plan, she added.

Data received from CRISP theChesapeake Regional Information System for our Patients, the area's HIE was also processed and synchronized into the system to ensure real-time availability of admissions and discharge information.

That is all part of phase one:patient identification. Phase two is interventions and outcomes. This phase requires RCCA staff to:

RESULTS

RCCA reports success with three key metrics.

First, billable transitional care management and chronic care management services now live in some of the practices.

"With targeted patient outreach, patient-specific CCM and TCM, and customized COVID-19 assessments, services were made available to patients after running rigorous risk-stratification protocols to filter out high-risk patients; 10% of the identified entire high-risk population for COVID-19 was validated by the practice by outreach and tele-connections," Alison explained.

Second, improvement in pain and advance care planning measures.

"We had timely interventions to close care gaps," Alison said. "The ACP measure requires patients to report the status of pain within 48 hours. The real-time pain assessments and scores help to close care gaps and ensure the patients are contacted within a specific time interval, 48 hours, to ensure patients' pain was brought to comfortable levels and satisfy the measure compliance."

And third, access to CRISP (Maryland's health information exchange) proved to be a game changer for the provider organization.

"Ease of integration was key," Alison said. "Embedding and onboarding of data from multiple sources, like EHRs, HIEs, claims, CCDAs, etc.,was a big plus to provide caregivers easy access to all types of data in one single place."

ADVICE FOR OTHERS

"Targeted patient outreach using preprocessed and intuitive data sets formed as a result of the summary of various clinical and nonclinical information can help optimize the utilization of staff or resources and thereby ensure better care outcomes and patient satisfaction," Alison advised.

"Inferences from data analytical tools work best in scenarios where data flow is not intermittent but continuous, real-time and unbiased, or deduplicated," she said. "In order to derive definitive insights that can help in decision-making and planning for the organization, the quality and quantity of data inputs is very critical."

Twitter:@SiwickiHealthITEmail the writer:bsiwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

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Machine learning helps cancer center with targeted COVID-19 outreach - Healthcare IT News