How intelligent data transforms health in the time of COVID-19

Dr. Liz Kwo breaks down the data and technology-enabled healthcare tools being adopted by next-generation payers and providers.
By Dr. Liz Kwo
12:00 pm

About the author: Dr. Liz Kwo is currently the staff VP of clinical data analytics at Anthem, and previously cofounded and served as CEO of telemedicine second opinion company InfiniteMD. She received an MD from Harvard Medical, an MBA from Harvard Business School and an MPH from the Harvard T.H. Chan School of Public Health.

COVID-19 has reshaped the way humans interact with technology in healthcare. Many countries have responded with public health measures such as social distancing, masks, and quarantine for suspected and confirmed cases. Intelligent data, technology, artificial intelligence (AI) and machine learning (ML) have started to lighten the burden to establish new ways for both health system supply and demand sustainability.

Among the most visible promoters of digital health are next-generation payers and providers (NGPPs). These companies, many of them created after implementation of the Affordable Care Act (ACA), have commonly adopted the belief that technology is the solution to improving the healthcare system. Their focus is on delivering technology-enabled solutions to improve clients, such as employers' – but more importantly, patients' as the end-user's – experiences. These companies are using concepts such as blockchain, telehealth, web applications and genomics.

How do healthcare companies deploy the intelligent, interoperable technology they need?

COVID-19 has prompted more healthcare organizations to embrace the idea of intelligent data as a tool for migration to digital health. Several companies are teaming up with startups that have the means to deliver technology-enhanced solutions either by allowing these startups to connect to their systems or by creating a customer-vendor type of relationship.

Connecting a smaller startup to a healthcare company’s mainframe or command center means the startup involved in the process must codevelop solutions customized to the system’s processes, flows, infrastructure and objectives. By accessing the company’s platforms, whether a payor or provider, through an electronic health record (EHR), a customer relationship management (CRM) system or other approach, the startup provides valid solutions based on the current status.

The solutions are not limited to identifying problems and providing fixes, but must extend to presenting innovations that enable the company to improve their patient records, deal more efficiently with aggregated and dispersed data, and handle a larger amount of information collected from various sources.

For example, collecting data run through predictive analytics via biometric devices that are connected remotely enables clinicians to complete their tasks at a higher speed with increased accuracy if the insights are actionable, potentially reducing medical costs if treatment or interventions can be determined correctly that are customized to the patient.

However, the downside of this type of cooperation is that it requires mobilization of massive internal resources, a relationship built on mutual trust and the willingness to share the risk among stakeholders such as providers, technology-enablement service companies and payors. Therefore, health companies look to startup companies for innovative solutions that can be easily integrated in their workflows with ready-to-implement solutions, such as technology enabling remote consults between patients and care providers.

AI, for instance, requires multiple levels of mutual agreement between the healthcare companies and collaborative startups for processes such as data sharing privacy and security, liability protections, validation of models, and ongoing monitoring of progress and milestones. The implementation of AI in clinics has specific requirements, such as the availability of a workforce trained to use digital tools, remote access to the latest technologies, and knowledge and willingness from both clinicians and patients to adopt these tools.

AI has produced valuable techniques that are in place and will continue to improve in triaging care via chatbots, models for data integration that enable a better and faster illness prognosis, predicting illness progression through medical records, and detecting anomalies for fraud prevention in claims or to ensure proper claims payments.

What digital data tools are already in place and on the rise?


Blockchain provides a change of secure and accurate information between nodes (e.g. patients, organizations and clinicians) with the help of a database that doesn’t require control from a third party. Blockchain enables organizations to conduct trusted transactions and to reduce their administrative costs by sharing a common ledger instead of maintaining their own, separate data.

All the organizations participating in the blockchain have access to the shared data and can instantly protect it if accessed by unauthorized users. In the healthcare sector, blockchain is still in the early stages, and faces challenges such as “the network effect,” meaning that all parties sharing the blockchain must be willing to work together in testing and evaluating the advantages of this technology, which is easier said than done.

Deploying blockchain for provider access can mean that patients who are not able to find a doctor in their network can have access to a wider pool of physicians, hence benefiting from faster and improved access to care.


Telehealth (also referred to as e-health, mobile-health or telemedicine) represents a means to remotely access medical services with the help of digital technologies such as laptops, smartphones and any mobile devices.

Among the objectives of telemedicine:

  • Increased access to services if the patient is isolated, has limited mobility or access to transportation, or resides in underserved areas without easy access to care.
  • Improved self-care and home management for more patients.
  • Access to patient data for a larger pool of medical specialists.
  • Improved communication and enhanced coordination between the various stakeholders in the healthcare system (providers, case managers, patient family, payers, etc.).

Examples of what telehealth can provide include:

  • Reminders via text messages, e-mail or phone regarding the need to perform various medical follow-ups such as taking a flu shot or scheduling a colonoscopy.
  • Online access for patients to receive medical information, such as test results, to request medical assistance via video conference, or request renewal of ongoing prescriptions.
  • Ability to upload medication lists, nutritional food logs, or pictures of skin rashes via phone, which can be reviewed remotely by providers (nurses, general practitioners or specialists).

Telehealth was used before COVID-19 in various forms as an instrument to train doctors in treating complex diseases in rural areas via video conference, such as the partnership between Medicaid and Extension for Community Healthcare Outcomes (ECHO) put in place at the University of Mexico, or as remote consultations with the use of symptom checkers defined by user inputs adopted in Spain by Mediktor.

COVID-19 brought a huge increase in Telehealth adoption. In the U.S., telehealth became used by 45% of consumers in COVID-19 era, compared to 11% of consumers in 2019 that called on this method to replace healthcare trips to the doctor’s office. Before the pandemic, the main players in the U.S. telehealth sector focused their services on urgent care areas by providing patients telehealth visits on demand. If before COVID-19 the estimated yearly revenues of U.S. telehealth companies were $3 billion, it’s estimated that in the next years up to $250 billion of current U.S. healthcare market could be invested in the virtualization of medical assistance through telehealth.

Telehealth, however, has its own challenges. Providers will need to adopt new ways of working; the exchange of concise and useful information must be improved; wider access and integration of technology is required; and clear data security measures must be in place. The effectiveness of telehealth compared to in-person visits will be closely measured, and reimbursement policies will have to be established and implemented properly.

For patients, the awareness and education of telehealth benefits must be understood, such as specific use cases to transmit valid information, the medical needs that telehealth can address, and understanding the insurance coverage of the service. These challenges can be surmounted, considering telehealth has the potential to bring benefits for patients, decrease costs for payers, increase efficiency for medical staff and improve overall healthcare experience.

Web applications

Web applications were developed as a response to the consumers’ expectations to have instant access to information. The healthcare market is currently dominated by two types of applications: ones that collect and record data regarding the health of clients (that can be shared with care providers and health insurance companies) and applications that provide access to health-related information such as health and wellness programs or provider and medical recommendations.

Mobile applications provided by some health insurance companies offer clients the opportunity to become more engaged in building their own team of healthcare providers, in order to compare prices of various services and to have autonomy in their overall health. With a few clicks, patients can locate providers inside and outside their network, choose an emergency room or urgent care center they need, or find a specialist that is available with a next-day appointment.

Many applications are populated with a huge database that delivers enormous amounts of information in multiple languages on topics such as diseases, symptoms and medications, and are reviewed by prestigious medical professionals. Other applications are focused on prevention, helping people maintain good health by focusing them on behaviors such as motivational exercise or mindful eating, as in controlling consumption by documenting the foods they intend to eat and the impact on their health. They can calculate the caloric intake and compare foods to make better decisions.

Aside from the benefits brought to patients, web applications help providers collaborate with their patients. Applications that measure and monitor patient heart rates and blood sugar levels are equipped with triggers that send alerts when indicators reach a certain level. The alert is sent to the doctors that monitor those clients. Based on the reading and the medical history, the doctors can suggest a next step. As more people adopt these web applications, the tools will only become easier and faster to use as they improve over time.


The human genome, considered the blueprint of the human body, holds the potential biological “plan” for each individual. The link between a typical genome versus variants that may lead to disease can be established by analyzing a huge amount of medical records and genetic data. This is a complex, time-consuming matching process.

The combined use of AI, machine learning and genomics brings to healthcare what has been missing in this discovery process: simplification and more accurate results in a lot shorter period of time. This can be achieved by integrating, for example, genomics with lab results, EHRs that include pathology, and imaging. T

This integration provides a more comprehensive look into a patient, which translates into better decisions taken by a supervising doctor and the improved ability to forecast disease and to provide customized treatment and medication based on prediction patterns and the efficacy of a medication for the individual. By improving the ability to predict and treat, the costs of healthcare can be reduced by eliminating unnecessary lab tests or ineffective treatments.

The challenge in utilizing genomic data is translating this knowledge to real-world use cases. Pharmacogenomics is the use of genetic testing to inform medication-management decisions, which can improve patient outcomes and reduce health cost. For example, genomic data is used to determine treatment options for cancer patients.

This type of precision medicine creates a new consumer healthcare market for people to determine the influencing factors of their health, with extraordinary levels of treatment personalization across various chronic conditions. As patients ask their providers for advice and payers for coverage in this growing market, providers will need clear training to understand the right tests to order, how to interpret the results, and how to properly inform their patients about the results.

The costs of these tests have significantly decreased over time, but an adequate understanding of the benefits is required to achieve actionable insights from the results.

Bottom line

The progress made in intelligent data and AI use in the healthcare sector is surging. It is vital to ensure that all patients have access and affordable healthcare utilizing efficient tools and customized treatments that rely on intelligent data for groundbreaking innovations in healthcare.


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