The widespread adoption of technology has enhanced the quality of human life in virtually all aspects. From a healthcare perspective,
machine learning (ML) has had a profound impact on the evolution of service delivery within the sector. For instance, advances have been made in medical imaging using machine learning algorithms able to process tremendous amounts of information at mind-boggling speeds.
- Technology has largely unexplored operational efficiency in healthcareOne of the major hurdles of healthcare systems is in managing patient flow – from entry to the point of discharge. If managed sub-optimally, patient flow can have debilitating ripple effects on the operational efficiency of a hospital. Some of these adverse effects include delay of patients in receiving proper care, occupation of the emergency department (ED) by inappropriate patients, increase in left-without-being-seen patients due to overcrowding, and delays in appropriate patient transfers.
- Incorporating technology in healthcare the right wayOperational efficiency calls for more than reveling practitioners in the bells and whistles of robust machine learning models.
- Have the end-user in mind when developing healthcare solutionsDesign thinking is a key methodology to adopt when mapping the problems to be solved in order to build realistic models for the healthcare sector. To build a data-driven organization to enhance organizational productivity and efficiency, it is acutely critical to have the end-users understand the need to be data-driven.
- Upgrade the healthcare sector curriculum to reflect new professional needsDigital skills are important to the success of using ML in the healthcare industry.Moreover, the existing workforce needs to be upskilled via continuous professional development and also be incentivized to learn more on their own. Apart from the generalists’ trainings, the healthcare system needs to further build capacity by finding, attracting and retaining data science talent.
- Strengthen data privacy and protectionData governance and security is one of the potential roadblocks to the adoption of machine learning solutions. Patients are concerned about the use of their personal healthcare data that will be collected using the digital solutions developed for the sector.? To curb this fear, healthcare providers need to set up strict governance around data management practices. These measures should be accompanied by data-sharing policies that are comprehensive, giving the data subject control of their healthcare data and anonymizing any personal identifiers where applicable.
In conclusion, the integration of technology into healthcare is not just a matter of convenience; it's a vital transformation that holds the promise of revolutionizing the entire healthcare ecosystem. From streamlining administrative tasks to enhancing patient care and optimizing resource allocation, technology serves as a powerful ally in improving operational efficiency.
As we navigate the evolving landscape of healthcare, the synergistic relationship between healthcare professionals and advanced technology will continue to shape a future where patient-centric, data-driven, and efficient care is not only achievable but becomes the new standard.
Embracing this evolution is not merely an option; it's a responsibility we bear to provide the best possible care to those who depend on our healthcare systems. The journey towards a more efficient healthcare future has begun, and technology is lighting the way.
