A combination of a simple website, mobile application and several separate software solutions operating in isolation, are often insufficient for a full-scale digital transformation. This case study describes a more viable solution known as a Software Ecosystem.
Focus21 worked with the Arizona based medical company NextMed, a company that provides treatment of kidney stones using a variety of comprehensive solutions depending on the patient profile.
NextMed was founded in 1996 by a group of healthcare practitioners to address the issues associated with using Lithotripsy (the use of ultrasonic shockwaves to break kidney stones for passage). To improve treatment efficacy, the clinic uses evidence-based methods based on statistical data, continual research, and use of the latest treatment protocols.
NextMed realized that to maintain their philosophy of providing the highest quality services, they needed to upgrade their use of digital technology and therefore turned to Focus21 for a solution. As a result, NextMed is now connected to a single ecosystem, a key feature of which is the implementation of Artificial Intelligence (AI) and Machine Learning (ML) technologies.
To start, the company needed a platform for registering their patient’s diseases, a system for tracking kidney stone treatments, and an online tool for effective training of technicians, doctors, and other health care providers.
The platform also needed to enable medical students, doctors, universities, and hospitals to compare the effectiveness between shock wave (ESWL) and laser (URS) therapy, based on patient demographic data and kidney stone disease parameters.
Another goal was to leverage the power of Artificial Intelligence and Machine Learning incorporating diagnostic imaging to detect kidney stones so they could be removed before they caused significant harm to the patient's organs.
Focus21 started the planning process by deeply immersing its team in NextMed’s current business processes and treatment workflows. They evaluated the staff’s user experience, analyzed large volumes of data that would be used for implementing artificial intelligence within the solution, and planned changes required to ensure software compliance with privacy requirements (HIPAA). Additional research was gathered from interviews with medical technicians and doctors. As a result, a thought-out software ecosystem was conceived and realized utilizing TAS (a modern application platform that provides a best-in-class developer experience).
As a result of the joint effort of Focus21 and NextMed, a true software ecosystem was created comprised of a diverse set of integrated tools that provides the most effective treatment of kidney stones. Key elements included:
Gateway and Portal: The basic entry point of the system, which provides unified authentication across all applications implemented within the TAS-based Platform. A control panel appears after user authentication that is customized according to the set of access rights assigned. All rights provisioning is controlled by an administrator who can create and distribute roles as needed.
Stone Decision Engine: An intelligent decision-making system that selects the preferred treatment method: shock wave (ESWL) or laser (URS) therapy based on the likelihood of success and complications determined from the collected patient data.
Simulator: Allows modeling the ESWL procedure for the best predicted outcome based on data from previous procedures performed for a patient.
Vision AI: The core of the ecosystem's artificial intelligence capabilities created for diagnostic images of kidney stones. Hundreds of thousands of images are processed and referenced to serve as a guide for accurately identifying kidney stones – as more images accumulate, the system gains further intelligence from which to make treatment recommendations.
TxPro: A separate desktop application created specifically for technologists that records patients’ reactions to shock wave (ESWL) or laser (URS) therapy. This application employs artificial intelligence as it continuously gathers new patient response data similarly to the Vision AI function.
Treatment Follow Up: Post-treatment results can be transmitted to identified stakeholders.
Reporting Tool: Produces downloadable quarterly statistical reports that compares current result values to best result indicators and serves as a measure for practitioners to determine treatment success.
The NextMed ecosystem solution demonstrated that it was possible to develop and implement a common access point to all desired information, a convenient authentication process and a division of data usage rights. A scalable structure for expansion was established in conjunction with a clear, universal UX for all products at an affordable cost.
The use of artificial intelligence and machine learning technologies throughout the system where feasible, proved over time to produce better treatment outcomes due to the evidence based guidance it provided.
In summary, the more a digital transformation project becomes complex, requires integrations of multiple solutions, and can leverage the application of AI, the more it can benefit from an ecosystem solution approach.
A combination of a simple website, mobile application and several separate software solutions operating in isolation, are often insufficient for a full-scale digital transformation. This case study describes a more viable solution known as a Software Ecosystem.
Focus21 worked with the Arizona based medical company NextMed, a company that provides treatment of kidney stones using a variety of comprehensive solutions depending on the patient profile.
NextMed
Healthcare
AWS
Docker
Electron
Flutter
GraphQL
Javascript
Python
React
Terraform
Product Strategy
Digital Transformation
Ecosystem Architecture
Software Architecture
Artificial Intelligence (AI)
Machine Learning (ML)
User Research & Testing
NextMed was founded in 1996 by a group of healthcare practitioners to address the issues associated with using Lithotripsy (the use of ultrasonic shockwaves to break kidney stones for passage). To improve treatment efficacy, the clinic uses evidence-based methods based on statistical data, continual research, and use of the latest treatment protocols.
NextMed realized that to maintain their philosophy of providing the highest quality services, they needed to upgrade their use of digital technology and therefore turned to Focus21 for a solution. As a result, NextMed is now connected to a single ecosystem, a key feature of which is the implementation of Artificial Intelligence (AI) and Machine Learning (ML) technologies.
To start, the company needed a platform for registering their patient’s diseases, a system for tracking kidney stone treatments, and an online tool for effective training of technicians, doctors, and other health care providers.
The platform also needed to enable medical students, doctors, universities, and hospitals to compare the effectiveness between shock wave (ESWL) and laser (URS) therapy, based on patient demographic data and kidney stone disease parameters.
Another goal was to leverage the power of Artificial Intelligence and Machine Learning incorporating diagnostic imaging to detect kidney stones so they could be removed before they caused significant harm to the patient's organs.
Focus21 started the planning process by deeply immersing its team in NextMed’s current business processes and treatment workflows. They evaluated the staff’s user experience, analyzed large volumes of data that would be used for implementing artificial intelligence within the solution, and planned changes required to ensure software compliance with privacy requirements (HIPAA). Additional research was gathered from interviews with medical technicians and doctors. As a result, a thought-out software ecosystem was conceived and realized utilizing TAS (a modern application platform that provides a best-in-class developer experience).
As a result of the joint effort of Focus21 and NextMed, a true software ecosystem was created comprised of a diverse set of integrated tools that provides the most effective treatment of kidney stones. Key elements included:
Gateway and Portal: The basic entry point of the system, which provides unified authentication across all applications implemented within the TAS-based Platform. A control panel appears after user authentication that is customized according to the set of access rights assigned. All rights provisioning is controlled by an administrator who can create and distribute roles as needed.
Stone Decision Engine: An intelligent decision-making system that selects the preferred treatment method: shock wave (ESWL) or laser (URS) therapy based on the likelihood of success and complications determined from the collected patient data.
Simulator: Allows modeling the ESWL procedure for the best predicted outcome based on data from previous procedures performed for a patient.
Vision AI: The core of the ecosystem's artificial intelligence capabilities created for diagnostic images of kidney stones. Hundreds of thousands of images are processed and referenced to serve as a guide for accurately identifying kidney stones – as more images accumulate, the system gains further intelligence from which to make treatment recommendations.
TxPro: A separate desktop application created specifically for technologists that records patients’ reactions to shock wave (ESWL) or laser (URS) therapy. This application employs artificial intelligence as it continuously gathers new patient response data similarly to the Vision AI function.
Treatment Follow Up: Post-treatment results can be transmitted to identified stakeholders.
Reporting Tool: Produces downloadable quarterly statistical reports that compares current result values to best result indicators and serves as a measure for practitioners to determine treatment success.
Thanks to the joint efforts of Focus21 and NextMed, it was possible to create not just separate applications, but a real software ecosystem that includes a full range of diverse tools necessary for the most effective treatment of kidney stones. It included six key elements. Gateway and Portal. The main entry point into the system itself, which provides unified authentication into all applications implemented within the TAS Platform. Plus, a portal, which is a dashboard that changes depending on the user's access rights. Stone Decision Engine. An intelligent decision-making system that chooses the treatment methodology: shock wave (ESWL) or laser (URS) therapy. It predicts the likelihood of success and complications based on patient information and data on all possible previous procedures. Simulator. The simulator allows the modeling of the SWL procedure based on data from previous procedures used for a particular patient. It allows the development of a truly effective treatment plan depending on various nuances. TxPro. A separate desktop application created specifically for technologists. It gives them the ability to record data about patients and their body's reaction to specific procedures when using shock wave (ESWL) or laser (URS) therapy. Follow Up. After the therapy is completed, the attending physician appoints a follow-up appointment to check the patient's condition. With this application, he passes on to the company information about everything that happened to the patient after treatment (for example, about possible complications during the recovery process). Reports. Quarterly reports are also an important statistical tool directly for their doctors and partner institutions. With this tool, you can compare current values with the best indicators, and if necessary, download the results in PDF.
The NextMed ecosystem solution demonstrated that it was possible to develop and implement a common access point to all desired information, a convenient authentication process and a division of data usage rights. A scalable structure for expansion was established in conjunction with a clear, universal UX for all products at an affordable cost.
The use of artificial intelligence and machine learning technologies throughout the system where feasible, proved over time to produce better treatment outcomes due to the evidence based guidance it provided.
In summary, the more a digital transformation project becomes complex, requires integrations of multiple solutions, and can leverage the application of AI, the more it can benefit from an ecosystem solution approach.
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