Artificial intelligence – created and introduced as something that could imitate the human brain and its capacity for cognitive thinking. Ever since its inception, AI has widely been used in developing various spheres such as selective defense, space exploration, and lately, intelligent personal assistant like Siri, Google or Alexa. While AI, no matter how powerful, can ever replace physicians, it is slowly making its way into modern healthcare and changing the realm of healthcare as we know it.
The boom in Artificial Intelligence
According to a recent report, the overall AI market will grow from 2016 at a CAGR of 62.9%, reaching $16.6 billion by 2022 in the US alone- and AI for healthcare is promised to be a major driving force. The potential for artificial intelligence in healthcare is enormous when it comes to making healthcare more patient-centric:
- Developing data-driven innovative solutions to improve the quality of care
- Integrating and analyzing millions of disparate and continuously evolving data points
- Implementing these insights in real-time and improving decision making at the point of care
Bringing machine learning to care
Agreed, robots and technology can never completely replace what physicians and nurses can do. However, we should entertain the fact that machine learning and AI truly hold the potential of improving outcomes, providing more accurate care, and basically transforming healthcare as we know it.
Imagine a patient walking into her PCP’s office complaining about pain. After listening to her symptoms, the PCP inputs them into his computer, pulling up her last test records and understanding the diagnosis and previous treatments. The physician would perhaps take a look at the patient’s vitals, her allergies, and her responses to previous treatments with smart algorithms helping him detect any anomaly too minor for a human to see. Finally, the physician would look at the patient’s medical history and family history and suggest a treatment that is specially cut out for the patient.
In truth, the advent of machine learning was based on the initial thought that it would be the answer to all data-related questions. However, in the past decade, healthcare has really progressed on making data accessible, and now providers are turning to machine learning.
Not only machine learning in healthcare is mature enough to improve diagnostics and predict medical events, but it has also only just scratched the surface of personalized care. Several companies and research facilities are using machine learning to identify cancerous tumors, diagnose diabetic retinopathy, and to identify events of readmissions.
Pioneering AI with Datashop
Datashop, Innovaccer’s proprietary platform is a powerful end-to-end solution for a comprehensive value-based care, and is designed with a practical machine learning perspective. Datashop strikes a balance between traditional analytics and machine learning. We use a Hadoop-based Integrated Data Lake streamlined with machine learning algorithms to integrate and analyze data and to derive useful information about diagnoses, care coordination and suggest preventive measures.
Datashop’s AI-assisted platform can help in:
- Predictive Care:
Datashop’s predictive data analytics can help the organization track its performance six months from now and see where it will stand in coming time. It could drill down to provider/facility level and indicate the work required on the weak links.
- Risk Stratification:
Datashop doesn’t just utilize clinical and claims data for risk stratification. By leveraging a healthcare organization’s utilization and SDOH data, Datashop’s smart predictive models can calculate and assign risk scores, quantify the immediate care interventions, and assist the providers in delivering targeted interventions.
- Coordinating Efforts:
If Datashop identifies ‘n’ number of at-risk patients and a certain number of patients being discharged from hospitals, it triggers push notifications and alerts for the health coaches and care teams for designing a post-acute episode. Providers have complete access to Datashop’s interactive dashboards where
organizations can track performance on every metric for the assigned set of
patient population and track the performance and impact of the implemented care plans, in real-time.
- Dynamic Care Plans:
The needs of patients, especially for chronic patients, vary and can range from transportation and finding social help to ensuring regular medications and managing clashing comorbidities. Also, the care plans can be adjusted as per the needs and goals of the patients. For example, if a patient with a hairline fracture in his ankle wishes to attend his son’s wedding that will be taking place in two weeks, the health coaches and the care coordinators can document the goal, develop a care plan inclusive of medications, physiotherapy sessions and assistance that would help the patient recover and attend to his short-term goal. In case the therapy doesn’t go as planned and the patient isn’t able to recover well in time, the health coaches can provide him with assistance like crutches, walker, wheelchair, etc.
- Organized and automated work queues:
Datashop’s strong machine learning algorithms that providers can use to reduce the manual labor associated with running through static files and create automated and intelligent workflows, based on filters on measures, performance, risk scores, or disease categories. These workflows match patients on various parameters like health status, risk scores, access to care, and assigns them to the most suitable health coach.
The road ahead
In no way will machine learning will ever replace the human torch in healthcare. It’s all about improving outcomes, enabling accurate care delivery and augmenting patient-centric care. If data from all over the place integrates into a single record, it will allow a clear understanding of patients and on a higher level- the population. If the results and outcomes are shared across the network in real-time and made available at the point of care, that will ensure faster decision making. If a physician can obtain results in a fraction of time, with an ideal degree of accuracy, that will ultimately improve patient care.
Someday, it will be common to have machine learning embedded in various processes to understand what’s going on with patients in real time. Sounds fairly futuristic. Datashop bridges this gap and brings the future to you, in the hopes that intelligent data-driven insights can improve the health of millions.