Digital Transformation in Healthcare: Benefits & Challenges

Artificial intelligence (AI)

Artificial intelligence is one of the most important technologies shaping the healthcare industry. AI is transforming healthcare by supporting early disease detection, improving medical diagnosis, personalizing treatment plans, and automating routine administrative tasks.

McKinsey claims that more than 80% of US healthcare organizations have adopted AI.

The real-time applications of using AI in healthcare include:

• Medical record processing: AI replaces time-consuming, labor-intensive tasks such as retrieving medical information, documenting medical records, organizing medical history, and billing.
• Drug discovery: Drug discovery is essential in healthcare because new diseases and infectious pathogens emerge. AI helps in drug discovery by identifying the most promising molecules and combinations that can interact with the body's biological targets from millions of molecules.
  • Case study: A study that appeared in scientific reports highlights that ChatGPT could assist in the early stages of drug discovery by designing and improving molecules that target EGFR and MCL1 proteins linked to cancer; the AI produced possible drug candidates and proposed modifications to existing molecules, after which the researchers assessed them using computational models and molecular docking.

    The results show how AI can help researchers identify and rank drug candidates with potential before laboratory testing. Experimental verification is still necessary to confirm their effectiveness and safety.

• Health assistants: Virtual health assistants play a significant role in monitoring patients' core vitals and symptoms. Chatbots serve as a communication channel where patients can ask questions without long wait times.

Machine learning (ML)

Machine learning is an AI technique that enables systems to learn from data. Here, computers learn from the training dataset and use that knowledge to make more accurate clinical predictions and decisions. It uses different algorithms depending on the dataset and the task the ML model is designed to perform.

For example, an ML model can be used for diagnosis; CNNs (Convolutional Neural Networks) are commonly used in medical imaging. The patient’s CT and MRI images are fed into the ML model; it detects patterns and anomalies. It is widely used to identify tumor markers in cancer diagnosis.

Internet of Medical Things (IoMT)

The Internet of Medical Things is a network of medical devices connected within the healthcare infrastructure. These devices and sensors collect patient data and provide valuable clinical insights to healthcare organizations and professionals.

Common IMOT use cases in healthcare include RPM (Remote Patient Monitoring), which helps monitor patients with chronic disease. Wearable IMOT devices track patients' vital signs, and healthcare organizations can view the data through mobile apps.

Big data and healthcare analytics

Big data and healthcare analytics are among the emerging fields in healthcare technology. Large collections of structured and unstructured data serve as input. Key sources of healthcare data used for healthcare analytics include EMRs (Electronic Medical Records) and EHRs (Electronic Health Records).

It uses data analytics to find patterns in patients' medical history, gene makeup, and biomarkers. By identifying patterns, it can predict disease risk and even its progression.

Blockchain technology

Blockchain is a decentralized network that stores and shares data, making records difficult to alter. Healthcare organizations primarily use blockchain to store medical records securely, as these records are a life-saving asset for patients. It also plays a vital role in tracking the drug supply chain in pharmacies.

Enhanced patient recovery

AI-enhanced robotic surgery has improved patient recovery by 15% and reduced complications by 30% compared with manual methods. Surgical precision has improved by 40%, directly enhancing patient recovery. Remote patient monitoring improves the recovery rate by providing continuous monitoring, improving follow-up care, and enabling personalized treatments.

Enable faster access to healthcare information

Digitizing health records is not enough; healthcare organizations must adopt a knowledge management system. It lets healthcare teams easily store, capture, organize, and retrieve information when needed. In healthcare, each piece of information provides valuable medical insight to support decisions and patient treatment.

Reduced medical errors

Medical errors, such as incorrect diagnoses leading to wrong treatment, bring life-threatening consequences. They erode patients' trust in healthcare providers, while technologies like predictive analytics and ML models improve clinical decision-making in diagnosing disease.

Streamlined clinical workflows

The Internet of Medical Things (IoMT) streamlines clinical workflows by connecting medical devices that automatically collect and transmit patient data to healthcare professionals.

For instance, a smart pulse oximeter automatically sends a patient's oxygen saturation and pulse rate to a clinical monitoring system, eliminating the need to record readings from a conventional fingertip pulse oximeter.

Reduced staffing costs

Administrative workers needed to perform manual tasks such as handling physical, paper-based patient records; their need is eliminated by digitizing the health record and having an EHR system.

Sanitation robots replace healthcare cleaning staff and remain reliable even in infectious wards. Overall, healthcare technologies automate manual tasks that are performed by healthcare staff and reduce the organizational cost of hiring and training staff.

Protecting patient data and maintaining privacy

Protecting patient data and privacy is challenging because ML models use patient data for training, which increases the risk of cyber threats. This happens because some ML models can potentially leak information about their training data through certain attacks.

Sending patient data to general LLMs can expose it to security breaches and threats, as it moves through public infrastructure.

Managing high implementation and technology costs

Acquiring the latest technologies like robotics, AI, and ML is highly challenging for emerging healthcare organizations. The initial setup cost is not affordable, and implementing these technologies requires additional infrastructure. This makes it difficult for developing healthcare organizations to invest in technological advancements. Beyond implementation, organizations must allocate a separate budget for ongoing maintenance.

Ensuring interoperability across healthcare systems

Maintaining Interoperability across the healthcare system is challenging. For example, a patient's vital signs from a wearable IMOT device must be updated in the EHR; if a healthcare professional misses this, it can disrupt the medical history and create problems with the treatment regimen. Maintaining healthcare data consistently across systems requires careful attention.

Workforce skills and digital literacy

Healthcare workers need workforce skills to handle healthcare technologies like electronic health records, medical devices, telehealth systems, and more. Digital literacy goes beyond operating a computer. It also includes finding, evaluating, managing, communicating, and creating digital information. Not every healthcare worker needs the same skills; they need different skill sets based on their role.

Organizations therefore need to assess digital skill gaps before implementation and provide role-specific training to healthcare workers.

Resistance to change and low trust

Healthcare professionals resist new technologies because they prefer existing workflows. Factors that prevent healthcare professionals from adopting new technologies include fear of learning them, lack of trust, and changes to established clinical practices. Clearly communicating the benefits and limitations of new technologies, demonstrating their reliability, and maintaining human oversight in clinical decisions can build trust. These measures help healthcare professionals feel more confident while adopting digital tools and integrating them into their daily work.

Legacy IT and inadequate infrastructure

Legacy IT systems and inadequate infrastructure can slow healthcare's digital transformation. Healthcare organizations often run outdated software that may not support AI, telehealth communication, ML, or patient monitoring. These technologies require a stable network, data migration support, upgraded servers, reliable network infrastructure, and other hardware. Therefore, healthcare organizations must invest in technological infrastructure, which adds to ongoing budgets and maintenance costs. Smaller clinics find it harder to afford than larger healthcare organizations.

Organizational silos and weak leadership

Healthcare organizations have different departments across the organization, making it challenging to collaborate and share information and healthcare records across teams. Therefore, healthcare organizations must consolidate data and ensure compatibility with digital healthcare technologies.

Healthcare staff must be assigned responsibilities and leadership to conduct training on using healthcare technologies. But this requires individuals' effort and time to train a community of healthcare professionals. As a result, training and leadership may be insufficient, and the outcomes may not meet expectations.

Delivering care through telemedicine

Telecommunication services are widely available, allowing patients to receive personalized care. In China, telemedicine reached 495 rural communities and over 500 rural residents. As a result, the accuracy of medical consultations increased by 30%, and access to medical care became 50% more convenient.

Using robotics to automate healthcare procedures and tasks

A nursing care center deployed UV robots to kill pathogens and microbes on the surfaces in shared rooms. The robots sterilized microbes after patients were discharged from each room and shared living space. The hospital found a decrease in infection rates after deploying the UV robots.

Monitoring patients remotely with connected devices

Griffin Hospital gave a connected kit to discharged congestive heart failure patients that remotely monitors blood pressure, pulse, oxygen, and weight for 30 days. The nurses reviewed and recorded the readings. The program enrolled 129 patients; 103 were monitored daily. Only 8 patients were readmitted (7.8% of 103); the previous readmission rate was 15%. Continuous remote monitoring halved the rate.

Using machine learning to turn patient data into early clinical warnings

The Geisinger Health System uses a predictive risk-stratification model to identify patients with chronic conditions who are at high risk of hospital admission within the next 30 days. A case study by the American Medical Association found that this method was linked to a 10% decrease in emergency department visits and hospital admissions among patients with chronic conditions. The organization also uses machine learning to identify patients at higher risk of colorectal cancer, enabling care teams to focus outreach and early screening efforts.

Using blockchain to make healthcare data exchange more secure and reliable

HealthChain is a blockchain-based electronic health record that stores patient data by providing shared infrastructure to manage and exchange health records securely. It supports common interoperability standards such as HL7 FHIR, helping different systems exchange health information more consistently.