According to a recent study, organizations that successfully implemented agile transformations saw an average 30% increase in key metrics like operational performance, customer happiness, employee engagement, and efficiency. The best method to stay nimble in the healthcare business is to employ insights into customers and markets to create data-driven decisions and influence a future roadmap and forecasts, basically the same parameters as any other industry.
Such information is widely available right now. Moreover, global data production is accelerating, especially in light of the dramatic rise in remote and hybrid working cultures after COVID. According to predictions by the International Data Corporation, the quantity of digital data generated between 2020 and 2025 will be more than twice as much as all of the data generated since the invention of digital storage.
Data helps in development
Patient information is being processed online more frequently in the healthcare industry, and COVID is mainly responsible for the emergence and expansion of virtual consultations. If the data is correctly combined, synthesized, and presented, data insights can influence the growth and innovation of industries.
Healthcare has an excellent opportunity to improve along with the quality, volume, and frequency of new data. New data points and insights are produced when outdated legacy systems and formerly analogue processes are digitalized. As a result, businesses may leverage their data to increase innovation, enhance workflow efficiency, provide better products, services, and patient experiences, and boost employee satisfaction.
These innovations have already been seen in South Africa’s public and private sectors. To increase efficiency and provide better patient care, the City of Johannesburg, for instance, declared in 2022 April that it would start digitizing patient information through an e-health system.
The long-term objective is to nationally integrate these digital health records with other systems, enabling the city administration to provide its inhabitants with more in-depth, trustworthy, and comprehensive care.
The launch of a fully integrated virtual consultation platform in June 2021 is another development. The method enables healthcare professionals to connect with patients regardless of whether they have medical assistance coverage or not, making home-based treatment accessible in South Africa.
The platform enables healthcare professionals and patients to connect with providers from other healthcare disciplines without moving between systems. It is accessible at any time from any device, including a smartphone/tablet/laptop/desktop.
Technology-based mental health solutions are also available. For example, the Panda app for mental health assistance, released a year ago, democratizes access to mental health care and assists individuals in finding the best treatment at the appropriate moment.
The app leverages its data to learn essential things about South African mental health, such as the urgent need for support for anxiety, depression, stress, sleep deprivation, and low self-esteem.
Machine learning addressing data issues
Still, there is a problem. So much of the data that these platforms and solutions are gathering and storing is unstructured and incompatible with other types of information. Therefore, the first obstacle is integrating all data across the organization and its services.
The second is choosing which data should be destroyed and which should be saved. The final step entails evaluating, categorizing, and interpreting the data for decision-making.
Automated analysis can help in this situation. However, the key to managing massive amounts of data and gleaning its meaning and value is machine learning (ML) technology.
Pharmaceutical, clinical research, and other healthcare organizations can gain a significant competitive edge from processing unstructured data with technology-driven solutions.
As an illustration, ML and artificial intelligence (AI) are being used to expedite drug development and guarantee that medications provide the best possible advantages to patients.
Large data sets may be processed and interpreted quickly by Machine Learning, which allows it to predict bioactivity, toxicity, and physicochemical qualities more accurately than human analysis alone.
Additionally, AI and ML can recognize qualities and characteristics in imagery that the human eye cannot remember or process simultaneously, reducing wait times and improving diagnostic precision.
Supply chains are being made better by using machine learning. To assure quality and safety, it can spot production bottlenecks, shorten batch disposition cycles, and keep an eye on in-line manufacturing procedures.
Using the cloud’s power
ML and AI technologies emphasize how they may enable executives to use previously untapped knowledge to make better strategic decisions.
Cloud computing is now more accessible and scalable than ever, which is a heartening fact.
As a result, healthcare organizations, from start-ups to multinationals, have access to personalized data solutions as more and more companies migrate to the cloud.
Businesses may quickly gather, classify, index, enrich, and visualize their data, whether it be physical or digital, using a cloud-native services platform. In addition, teams may present their data in a digestible style and extract the information pertinent to their needs using this application, which uses machine learning technology and Google’s AI capabilities.
When properly analyzed, data can reveal necessary information about patients, treatments, administrative procedures, and more. Because there is a greater probability than ever before that data analysis may lead to the next significant medical advance, the South African healthcare sector should be utilizing the technology available to it.
South African enterprises must stay up or risk falling behind due to the market’s quick and constant change brought on by digital transformation and the rapid advancement of technology. Business success depends on agility, and the healthcare sector is no exception.
Fundamentally, HealthCloud offers a platform that enables numerous dispersed stakeholders to join their data sets into a larger, more sophisticated data structure. In addition, with healthcare organizations and insurance partners able to utilize APIs to trade health record data more meaningfully; all the heavy lifting is done in the background.
Traditionally, the insurance underwriting process has been a difficult task that requires agents to gather health information on potential clients from various sources, necessitating a sizable amount of human labour. This is a difficult task that wastes time and is vulnerable to human error. However, for the insurer to assess the risk involved and provide more accurate pricing and product offerings, they must have this information.
Structured health record sets can now be integrated directly into an insurer’s underwriting procedure thanks to HealthCloud. A complete picture of a consumer’s health profile can be obtained using the HealthCloud platform, which combines insurer data with electronic health records, pharmaceutical use, pathology results, remote diagnostic tools, wearables, and facial scanning (rPPG). This enables the insurer to comprehend risk more quickly, accurately, and affordably than is now possible.
Health education
This technology’s benefits on the underwriting procedure can be attributed to the lessons learned in the healthcare industry. Creating a platform that combines health data has been a huge endeavor because health data is dispersed throughout the world, unstructured, in various formats, and in many databases, if it exists at all.
With the current API design in place, even the most fundamental IT systems, like those frequently found in medical practices, can now interface with the platform thanks to the evolution of the HealthCloud technology. As a result, along with improving care, diagnosis, and clinical decision-making outside the insurance vertical, such parties can share and exchange improved data sets.
Similar difficulties confront insurers. To speed up and improve the process of applying for new policies and processing claims, claims and underwriting must immediately harness all types of first- and third-party data, such as health information, identification verification, and industry risk screening. This necessitates intelligent underwriting and claims solutions that integrate cutting-edge technology like intelligent automation and machine learning, allowing life insurers to conclude the majority of situations in only a few minutes.
As a result, underwriters save time on the more “routine” daily decisions, freeing them up to concentrate on the responsibilities to which they can best contribute. Both open-book and closed-book providers can use this. The time savings from speeding up underwriter decision-making are substantial. It all boils down to improving each company’s underwriting process’s cost-effectiveness, accuracy, and efficiency.
Putting insurance into practice
The health cloud Platform links to a wide range of data sources for insurers, resolving data interoperability issues and offering a unified view of a consumer’s unique health profile. The HealthCloud Platform combines insurer data with electronic health records, prescription use, pathology results, remote diagnostic tools, wearables, and facial scanning (rPPG) to give a complete picture of a policyholder’s health profile.
Insurers are instantly given a Health Risk Score thanks to proprietary algorithms that use artificial intelligence (AI) and machine learning (ML) to better understand and manage risks associated with new applicants and current policyholders, including fraud/non-disclosure. When managing their portfolios and risk profiles, insurers may manage them more accurately thanks to access to this completely consented data sharing. The South African life and health insurance businesses have been firmly rooted in this technology-driven approach to data analysis for many years.
For new policy applications, continuing claims management, and policyholder engagement, HealthCloud offers real-time health data. SilverBridge offers cutting-edge automation technology for its part. Together, these factors produce the Smart Underwriting solution, which offers insurers intelligent automation of the insurance underwriting process by securely integrating health data and medical records to more precisely and consistently analyze the risk of enrolling individual clients. This gives insurers the tools they need to take advantage of clever automation of the underwriting process. The platform achieves this by securely combining pertinent internal and external data used for underwriting and enabling automated choices.
Automating these operations improves accuracy and results in cost savings by removing the possibility of human error. For instance, using new technology can cut the price of underwriting a policy by roughly 80% (where medical reports are necessary). In addition, to date, more than 2.5 million distinct Health Risk Scores have been generated, giving insurers access to a sizable collection of data that will help them to further improve their value proposition.
Following South Africa’s example, in Mexico, Clinicas de Azucar has deployed artificial intelligence to gather a variety of patient data; analyze that data; and make recommendations to over 150,000 Mexicans who run the risk of becoming diabetics. As per a report from ArabianBusiness, in 2021 alone, USD 44 billion was raised globally in health innovation, twice as much as 2020, and the acquisition of health and health tech companies rose 50%. And expect this number to grow, given the fact that AI and ML have been emerging as successful solutions in the global healthcare sector and the industry will witness a massive investment wave, along with more technological innovations, as the world gears up for future healthcare challenges (including pandemics).