Shaping a digital future for healthcare

The global healthcare industry is at an inflection point, facing constrained budgets, clinician burnout, and a projected global shortfall of 10 million healthcare workers by 2030. While regulatory complexity, fragmented ecosystems, and legacy clinical systems have slowed digital adoption, momentum is shifting – 90% of C-suite executives now expect the use of digital technologies to grow.

A driving force in the current global technological advancement is Artificial Intelligence (AI), which, alongside traditional machine learning and deep learning, is projected to result in net savings of up to USD 360 billion in global healthcare spending. AI is increasingly enabling predictive care models, clinical decision support, and improved operational efficiency across healthcare systems. This blog will explore how digital solutions, specifically cloud computing, automation, and data and AI, can help transition the industry from reactive care to a more proactive, data-driven model.

According to McKinsey & Company, “The goal of digital and AI transformation is to fundamentally rewire how an organization operates, building capabilities to drive tangible business value… through continuous innovation.” Here are some challenges faced by the healthcare industry worldwide, which can be targeted as primary points of change through digital transformation.

• The workforce gap

The global healthcare workforce is facing a ‘triple threat’ of hiring difficulties, talent shortages, and increasing clinical burnout. As a result, many healthcare professionals spend significant portions of their time on administrative and operational tasks rather than direct patient care, placing additional strain on already stretched teams.

• Legacy infrastructure

Outdated infrastructures surrounding workflows are a key factor that holds institutions and larger industries back from better efficiency. Many health systems worldwide continue to rely on outdated workflows, such as fax machines, manual phone calls, and paper-based records. These monolithic legacy systems are often difficult to untangle, thereby hindering digital transformation.

• Budget and scaling hurdles

Another common concern among executives and decision-makers is budget constraints, with 51% of healthcare leaders ranking it as a top obstacle to scaling digital investments. While there is strong industry enthusiasm to modernize workflows, studies show that 75% of healthcare leaders believe their organizations have not sufficiently planned or allocated resources to achieve their digital transformation goals. Initiatives also often stall when organizations struggle to demonstrate a clear return on investment. Establishing a strong business case early on is therefore critical to prioritizing initiatives and ensuring long-term value.

• Data and trust

It has been noted that fragmented data silos and poor data quality delay value creation. Additionally, there is a ‘trust gap’ among both professionals and their clients in different industries, about ‘AI hallucinations’ and potential data bias. This issue is pertinent in the healthcare industry, which places great emphasis on the accuracy of data and projections. Addressing these concerns requires strong data governance frameworks, high-quality datasets, and AI models that provide transparency and explainability for clinical use.

Despite ongoing challenges in the healthcare industry, leadership has made significant strides in digital transformation in recent years. Across both technical and non-technical roles, a majority of executives consider digital and AI initiatives a high or top priority for their organizations. Reflecting this focus, many healthcare organizations are investing in core technologies such as Electronic Medical Records (EMRs) and Enterprise Resource Planning (ERP) systems. These investments are yielding tangible benefits, with a large proportion of executives reporting high satisfaction, particularly with robotics and advanced analytics, demonstrating the value of strategic digital initiatives.

At the forefront of this transformation are cloud computing, automation, and AI. While being relatively new to the healthcare sector, these tools have been utilized and developed for years among businesses in various other industries, generating a wealth of information on how they can be integrated into healthcare facilities.

• Cloud computing

Cloud-based models offer advantages such as greater scalability, reduced capital expenditure, and lower operational overhead. This is especially useful for healthcare institutions, since cloud computing in health care can also provide the on-demand computing power and storage required for data-intensive applications, eliminating upfront capital investments in hardware.

• Automation

Organizations across the globe use automation to increase productivity and profitability, improve customer service and satisfaction, reduce costs and operational errors, adhere to compliance standards, optimize operational efficiency and more. In healthcare automation, digital tools can free up 13% to 21% of a nurse’s time (240-400 hours per year) by automating low-value administrative work. Automation is also increasingly used in areas such as supply chain management, pharmacy dispensing systems, and patient triage workflows.

• Agentic AI

Agentic AI is capable of completing complex, multi-step tasks with minimal human supervision. The Deloitte US Centre for Health Solutions notes that, “Autonomous GenAI (Generative Artificial Intelligence) agents, could be used to help automate some of these tasks, enhancing the efficiency and productivity of administrative staff while reducing the health system’s costs.”

It is also noted that GenAI can streamline healthcare operations by automating tasks like electronic health record updates, improving patient flow, and enabling predictive modelling for crisis preparedness. Moreover, by automating administrative tasks and providing patient support, GenAI frees up healthcare professionals’ time, allowing them to focus on direct patient care.

• Clinical AI impact

The impact of AI in generating effective clinical analysis and results has been transformative. It has been observed in studies conducted in the USA that algorithms assisting in CT, MRI, and X-ray analysis currently represent more than 75% of FDA-authorized AI-based devices. AI applications are also expanding into areas such as pathology analysis, clinical risk prediction, and treatment optimization.