How PIN.care Optimizes Disease Treatment with Splice Machine
PIN.care is a physician network that helps doctors profitably drive better patient outcomes using digital medical data and artificial intelligence (AI). It also provides the pharmaceutical industry with multi-dimensional data for clinical research and offers insurers hard-to-process, timely data that helps them maintain healthy populations and reduce medical losses.
PIN has created a unique and powerful network of neurology clinics, creating a comprehensive and growing database of thousands of disease attributes. With a focus on objective digital testing, PIN addresses the unmet needs of patients by using large amounts of pooled, anonymized data to improve their care. By linking many clinics together, PIN.care exponentially increases the amount of information it has about the trajectory of the disease, treatment protocols, and efficacy — and it uses all of that information to increase positive outcomes for all of its stakeholders.
Up until very recently, limitations in database technology severely hampered PIN’s ability to fulfill its mission. Now, with Splice Machine, PIN can ingest and process petabytes of different types of data at lightning speed and make real-time treatment recommendations — all while the patient is in the doctor’s office for her checkup. Dr. Mark Gudesblatt, a well-published Neurologist and PIN.care’s Chief Medical Officer, says “today, the neurological community does not leverage the multiple dimensions of available digital information to treat neurological disease.” His vision is to change that with data and with AI.
Neurological disease is very difficult to diagnose and treat. Symptoms erratically come and go, and can include depression, reduced brain function, impaired mobility, spasticity, poor balance, fatigue, bladder and bowel dysfunction, slurred speech, cognitive problems, and so much more.
Traditionally, doctors have used a combination of patient history and a neurological examination to treat it. But these neurological exams are not sensitive enough, quantitative enough, or easy enough to document.
The neurologic community has long needed a paradigm shift to incorporate improved techniques and technologies to address these unmet needs. For example, doctors in the past could identify unique disability trajectories associated with individual diseases — but only if they could better quantify the impact and progression of the disease over time in a multidimensional, objective manner. Luckily, recent improvements in digital instruments have made it possible to quantitatively measure functional impairment across cognition, sleep, pulmonary function, vision, manual dexterity, gait, and more — regardless of the specific disease.
PIN collects all of this digital data directly from IoT instruments and patient records, de-identifies this data, ingests it into a Splice Machine database, and makes a personal treatment protocol recommendation for each patient.
Enter Splice Machine
So when PIN.care needed a data platform to enable IoT data collection, predictive machine learning, and operational advisory applications, the choice was easy. It chose Splice Machine because of Splice Machine’s unique combination of:
1. Scalability
With the growth of IoT system data, the digital attribute set will grow quickly. PIN will also rapidly grow its network of clinics to add thousands of more patients in the coming years. The database of record will have to seamlessly scale as the network grows. Splice Machine is a distributed, scale-out architecture that spreads data and computation across many computers to perform efficiently.
2. SQL
There is a need to slice-and-dice data for subscribers across many different dimensions as well as create Machine Learning pipelines. SQL is the standard tool for analysts to manipulate data without having to learn new distributed data technologies, and it is the lingua franca for application developers to build concurrent applications. Splice Machine is fully ANSI SQL compliant and enables PIN to leverage its existing SQL-trained staff.
3. Built-in ML and ML Manager
The Splice ML Manager provides end-to-end lifecycle management for developing, training, and testing ML models, thereby streamlining and accelerating the design and deployment of intelligent applications using real-time data.
Benefits
At least 5 groups in the continuum of neurological care benefit from Splice Machine’s unique ability to aid in the diagnosis and treatment of Neurological patients:
1. Patients
By synthesizing and acting on all of this data, Neurologists can achieve the “holy grail” of patient treatment by understanding exactly where the patient is in her journey, predicting the symptoms that are coming next, and prescribing the right medicine for that moment.
2. Payors
Because they are paying fewer claims against a healthier population, payors are improving patient outcomes while simultaneously maximizing financial objectives.
3. Providers
Because all of the tests are coded reimbursable office visits, doctors can profitably improve patient outcomes.
4. Regulatory Agencies
The network may also be used to demonstrate the efficacy of various treatment methods with regulatory agencies because its output is objective multidimensional data that will effectively show the patient outcome improvements.
5. Pharmaceutical Companies
The PIN data will be used by pharma companies as part of their clinical research in trials, saving them millions of man-hours (and millions of dollars) because the data is collected automatically and de-identified, satisfying GxP compliance guidelines.
As Dr. Gudesblatt says, “…the use of such an objective multi-dimensional data approach in the real world will allow improved therapeutic trials, identify the sweet spot of therapy efficacy, identify success vs failure earlier and less expensively, and allow exploration of novel disease states for similar critical junctures.” Splice Machine is proud to support the needs of the neurological disease population and help them live a healthy, happy, full life.
To learn more about the work of PIN.care, click here for a case study.
