Tackling Emerging Challenges With Data-Driven Innovation in Digital Health

By Bal Heroor, CEO and Principal, Mactores
Twitter: @mactores

The healthcare industry is rapidly embracing digital transformation to enhance service quality, optimize data-driven operations, and enhance profitability. As healthcare organizations adopt AI, cloud computing, and cybersecurity software, the hope is that these new technologies can address the many persistent challenges faced by the industry.

To better understand these challenges, looking at their search data is helpful. Mactores has analyzed the web searches made by decision-makers at thousands of healthcare companies in the last three months to uncover their most pressing concerns for the year. Here’s what we found.

Personalized care and outcomes

Healthcare organizations strive to deliver personalized patient care and are increasingly looking for new ways to do it. According to an Accenture report, 77 percent of healthcare executives believe personalized care will become the industry standard by 2025. As a result, healthcare providers are looking for advanced technologies to generate data-driven insights and more personalized care.

One factor driving the implementation of more personalized care is integrating genomic data into patient care, incorporating an individual’s genetic information to provide more precise and tailored medical treatment.

Other advances include predictive analytics for disease management, which involves using data-driven algorithms to forecast the likelihood of a specific disease or health event occurring in an individual. Other initiatives include telemedicine and remote health monitoring, value-based health care models, and cross-collaboration among health care providers.

Healthcare organizations are also actively exploring AI and machine learning (ML) to enhance patient care personalization by leveraging and analyzing large amounts of data to predict clinical outcomes and enable more informed decision-making. To build AI/Ml platforms, healthcare companies must build comprehensive data platforms to enable their data science and research teams to access reliable, secure, and high-quality data. Such data platforms would be highly scalable and low-cost to support ongoing experimentation and research.

Cybersecurity and data privacy

Of course, concurrent with this increased adoption of digital technologies in health care has been a surge in cybersecurity risks and data privacy concerns. In 2022, the average cost of a single healthcare data breach surpassed $10 million, which represents a 40 percent increase over 2020. And that number is only expected to climb in the coming years.

Healthcare organizations are lucrative targets for cyber attackers. They also face multiple challenges in securing their digital infrastructure, including protecting sensitive patient data, compliance with data privacy regulations, ensuring secure data exchange among providers, and implementing robust access control mechanisms. Healthcare organizations are adopting advanced cybersecurity systems and adopting data privacy frameworks to address these concerns. A few examples of such cyber security systems involve data discovery tools that can classify patient data and obfuscate critical patient identification data to avoid any leaks or bias for research. Another example is to build attribute-based access control to add tags to the datasets, which can be assigned to relevant teams easily to unburden the security team from manually assigning security over the datasets.

Operational efficiency and resource utilization

Today’s healthcare market is in a significant state of flux, and healthcare organizations need to optimize their operational efficiency and resource utilization to remain competitive and sustainable. A Goldman Sachs report found that by 2025, digital transformation in health care could save the industry $300 billion.

However, key challenges on the path to achieving operational efficiency remain. These include: streamlining workflows, automating manual processes, adopting data-driven decision-making,
implementing telehealth and remote patient monitoring, optimizing supply chain management, and reducing administrative and clinical waste. To tackle these challenges, healthcare organizations increasingly leverage analytics, AI, and ML to optimize and automate operations, removing the burden from overworked staff.

The common denominator among these challenges is the need for a comprehensive, data-driven approach to health care. Establishing an integrated data infrastructure and adopting advanced analytics capabilities will empower healthcare organizations to tackle the challenges of personalized care, cybersecurity, and operational efficiency. By embracing digital transformation, the healthcare industry can continue to evolve, innovate, and deliver improved patient and provider outcomes.