In this blog post
AI Powered Advanced Analytics for Healthcare Intelligence
The challenges of rising costs of care, and the big shift towards value-based care models has given rise to the need to leverage data effectively to enable data-driven decision making. Joining the dots in healthcare data for meaningful, actionable insights has become imperative. The enormous volumes and complexities in the data necessitate the use of Artificial Intelligence and Machine Learning (AI/ML) to mine information from them. AI powered advanced analytics can drive process efficiencies, cost reduction, and can also provide financial, operational, and clinical intelligence for smarter healthcare strategy. More importantly, predictive and prescriptive analytics enable the move towards ‘Wellness Care’ from ‘Sick Care’.
GAVS conducted a webinar earlier this year on ‘Enabling an Intelligent Healthcare Organization through Healthcare Analytics’. The eminent speakers of the webinar were: Mr. Saji Rajasekharan, Vice President – Software Engineering, Clinical Intelligence at Premier, inc., and Mr. Srinivasan Sundararajan, Vice President – Technology at GAVS Technologies. This blog includes some of the key discussion points and takeaways from the webinar. The link to the entire webinar can be found at the end of the blog.
Changing Trends in Healthcare with AI
The rise of technology in the pandemic era and the increasing use of AI/ML to leverage healthcare data for shaping the delivery of services is definitely a positive trend. Hospitals and healthcare providers are witnessing changing patterns in patient care and management solutions in the post-pandemic era. Despite being in the early stages of AI adoption, there is considerable growth in the use of technology in healthcare. There are AI-based systems for precise disease diagnostics, anomaly reduction, reinforcement planning, and IoMT devices that provide continuous patient data. These emerging uses of AI play a major role in creating a complete ecosystem of value-based care for patients. They are also creating new trends in healthcare like:
- Use of population health to improve health outcomes of a defined group of individuals through improved care coordination and patient engagement
- Addressing data privacy through de-identification and anonymizing of healthcare data
- Access to virtual care or telemedicine
- Patient health passport and traceability to support global healthcare outreach
Technology as an Enabler for Integrated Healthcare
The world is moving towards decentralized sharing of patient data with an emphasis on interoperability. Recent growth in technologies such as Blockchain have been significant enablers for providing traceability in healthcare. In the absence of technologies like Blockchain in the traditional world, healthcare providers relied on centralized databases, which affected data interoperability in countries with large populations such as India.
Another trend that is making significant headway from a data perspective is Graph Technologies. The patient ecosystem needs to be understood better for healthcare providers to treat the patient better. Data typically captured as part of an EHR platform needs better analysis and interpretation. Healthcare providers must focus on collecting data from various sources that needs to include personal medical devices and the social environment of that patient. Evidently, these new uses of technology will play a significant role in shaping the patient-provider relationship today and in the future.
Data Analytics for Better Service
From an analytics perspective, AI/ML is predominantly used in predicting the patient’s conditions – for example, predicting readmission rates. Using data analytics on patient records can help predict readmissions and to take suitable preventive measures. Another area where data analytics can help is in fraud prevention. Many hospitals spend a considerable amount of time processing insurance data ensuring that the data is accurate. As a result, there will be a reduction in operational expenses made possible through operational efficiencies.
Role of AI/ML and Blockchain in Healthcare
There are multiple touchpoints for data — PHR data, remote monitoring devices, wearable devices, etc. The surplus of patient data has given birth to two needs — the ability to mine relevant data and secondly, to be able to extract inferences without heavy human dependency. As data integration continues to be one of the critical aspects of focus among healthcare providers, AI/ML-based technologies such as NLP and Business Intelligence have stepped up and proven to be the preferred solutions. On the other hand, interoperability and consent management can be effectively driven by Blockchain. As scalability is one of the primary areas of concern, Blockchain is proving to be a viable solution as there is no need to centrally store all the data. With the decentralization of data, healthcare providers can access data irrespective of geography.
There are several other areas where technology is evolving to play a major role in patient care. Some of them are:
- Telemedicine cuts geographical barriers as doctors can use chat-based or voice-based apps to treat patients
- Emotional Intelligencecapabilities can help overcome language barriers between doctors and patients
- Better Communication between doctors and patients as AI can help with OCR recognition, handwriting, and online translation
- Real-time AI and ML-based algorithms can help Drug Research with graphing, data modeling, and anomaly study
- AcceleratedProduct Development by investing in blockchain skills
- Clinical Trialsneed the ability to have tamper-proof records, and Blockchain has proven to be a great candidate.
New trends to look out for:
- There is a paradigm shift towards wellness care. Healthcare providers are focusing on bringing mindfulness and including mind and spirit into the realm of health. Companies are offering incentives to employees to embrace that line of thinking. As a result, value-based care such as telemedicine will play an important role in creating digital transformation in this segment
- The move from applications to analytics-based problem solving will help innovation that directly impacts time-to-market
- Robust digital access management solution to be able to provide the right access to the right data and to ensure that all vulnerabilities are well tapped and under control
- AI/ML solutions to predict healthcare outcomes through deep learning
The possibilities with AI/ML in mining healthcare data for the betterment of healthcare are limitless. GAVS extensively leverages AI/ML, Automation, Big Data, Cloud, and Blockchain technologies to accelerate healthcare digital transformation. To learn more about our offerings, please visit https://www.gavstech.com/healthcare/. GAVS is currently working on multiple use cases with clients in healthcare, focusing on interoperability and Blockchain. For more on such offerings, please visit https://www.gavstech.com/service/gavs-blockchain-services/.
As mentioned earlier, some of the content in this blog is based on a webinar hosted by GAVS earlier this year. You can watch the entire webinar here. GAVS routinely organizes insightful webinars with GAVS’ tech leaders, the leadership team, and industry thought leaders to explore current and emerging trends. To watch all our webinar recordings, please visit https://www.gavstech.com/videos/.
We have been hearing and using the term ‘Big Data’ for a while. Though there could be multiple interpretations of it, one common explanation is, Big Data represents the acquisition, storage, and processing of massive quantities of data beyond what traditional enterprises used to. Also, the data need not be in conformity to any schema, business rules, or structure.
In the recently announced Technology Trends in Data Management, Gartner has introduced the concept of “Data Fabric”. Here is the link to the document, Top Trends in Data and Analytics for 2021: Data Fabric Is the Foundation (gartner.com).
As per Gartner, the data fabric approach can enhance traditional data management patterns and replace them with a more responsive approach. As it is key for the enterprise data management strategy, let us understand more about the details of data fabric in this article.