With the growing adoption ofdata analytics在医疗保健领域，我们更清楚地看到了数据的两面性——大脑和体力，可以这么说。多年来，我们可以访问原始数据——每家医院的数据量高达50pb，每年数据收集量增加了36%。但是，如果我们不能分析和消化原始数据，它对我们没有任何好处。每家医院的50pb数据中，目前只有5%被访问和分析。
This context of limited use of data, daily at point-of-care, gives us a great opportunity. Imagine a world where all of the data generated is used to provide personalized care to each patient at both the larger population and individual levels. With this opportunity, we can use the power ofartificial intelligence(AI) to make it a reality.
Challenges Ahead
AI has great potential, but to get to the promised land, we need to cross certain barriers. First and foremost, there are technological barriers to overcome. For AI solutions to work, clean, pristine data is required. Far too often, this high-quality data just is not available. In many ways, we are data rich, yet information poor. This is a notable issue since data quality can dictate the type of outcomes obtained by the applications that depend on it. Therefore, opportunities abound for tech enablers to assist in this capacity.
The second technology barrier we need to overcome is the process of integrating data from multiple sources to generate deep knowledge, or an algorithm. Additionally, integrating the algorithm within the clinical workflow is a challenge that needs similar attention. So, even if we have the technology to acquire the right kind of data and build an AI algorithm, we still have to determine the best way to adopt it into the workflow of each appropriate individual.
Lastly, we cannot underestimate the level of change and adaptation we need to make as people, which will help with both the adoption and scaling ofAI solutions. A common misconception of artificial intelligence is that the technology will remove the human element completely from care. This is far from the truth. With the amount of data that is being generated, it is hard for the human mind to process and connect all the information and make the right diagnosis or decision. Additionally, the skill and expertise levels are the not the same across the world. AI and technology can help with accurate diagnosis, leveraging large amounts of data in a consistent fashion.
How Data Meets These Needs
可以说，虽然我们拥有所需的所有原材料，但要用它们建造一座房子，需要更高水平的工程和技术。第一步是解释数据，以表明我们可以对工作流程进行的可能改进。例如，数据分析可以通过查看可用的设备并最大限度地利用可用资源来提高成像调度的效率。
此外，通过观察数据和有效成像，我们可以更好地进行高质量的成像，减少让人们回来重新扫描的需求。我们还可以通过人工智能对影像进行分类，并将相关的扫描结果发送给放射科医生，以更快地进行诊断和治疗。All of this affects theclinical workflowbetween technicians, radiologists, oncologists, nurses and other staff members. The ripple effect is evident.
When it comes to the inconsistencies of diagnoses, that challenge points toward the growing trend of healthcare democratization. If we have the data available and can pull in more real-world information, we can be more consistent with our diagnoses and treatments. This, of course, requires quality data that is shared across platforms and systems. So, the more cases it can analyze and the wider the scope of the care, the more accurate the AI, and the more effective our care will be.
Seeing the Bigger Picture
On the surface, the amount of work that will be necessary to effectively overcome these obstacles might seem overwhelming. Yet, there is no single company that is going to be able to meet these challenges alone. It is safe to say that a collaboration of major players and startups will be needed to implement the type of sophisticated infrastructure required to put data to work successfully. It is important for companies to build “roads and bridges” that will allow them to partner well and deliver the best innovation possible through existing platforms and ecosystems. It is important to build the roads and bridges to allow innovation to quickly get to patients, clinicians and providers. The opportunities are endless.
The goal is to developplatforms、应用程序和流程，其功能强大到足以激活数据以实现更好的医疗保健，并有效地将临床研究转化为临床实践。通过设计一个生态系统，将数据和AI算法无缝集成到日常工作流程中，可以通过各种方法实现这一点。考虑到这一功能，至关重要的是形成伙伴关系，将利用最好的创新，无论它是在哪里创建的，以确保更稳健的运营效率，临床生产力和为所有客户获取价值。对于我们这些在医疗和数据科学领域工作的人来说，这是一个激动人心的时刻。齐心协力，我们就有机会彻底改善医疗保健。
What the Future Holds
更好的过程和更好的数据的最终结果是更好的治疗我们的病人，但结果超出了治疗本身。虽然很多人担心临床医生一听到“人工智能”就会被取代，但现实是，人工智能实际上会让临床医生与患者有更多的视频时间。研究表明，美国的临床医生目前只花费27%的时间为患者提供实际护理，而在英国，这个数字可能低至13%。With AI aiding in processes, scheduling, triage and general data “wrangling,” there is more time for human interaction,
which is what humans do best.
After all, direct care is the reason many clinicians enter the field in the first place? Burnout is real, and overworked teams are as prevalent as ever, especially in the times of aglobal pandemic. Giving clinicians respite from the administrative work and allowing them to thrive in the fulfilling work of medicine will benefit all parties, including the patient.
Not only can AI help the physician’s time and efficiency, ultimately it will help increase the quality and outcome for patients. For example, in the space ofoncology, decision-making is complex from diagnosis to treatment options. We also know some treatments work better for some patients than others. In this context, AI has the potential to distinguish between patients and suggest the best treatment for each individual. Likewise, it can also alert when a treatment is not working much earlier than what happens today. Such an early indication could not only help with the patient’s outcome, but could also benefit the patient financially, since certain treatments still in the early stages of development may not be reimbursed.
Data is the raw materials to build a house, but the proper data analysis and processes are what help those materials become a home where people can live and work. And just like one person alone cannot and should not build a house, so one company, app or silver-bullet solution alone cannot make this transition to fully adopted data analytics for the healthcare industry. We need a collective willingness to dig into the resources we have available to help refine our processes and our care. When we do that, we will have a new home in the new era of data and AI adoption.
AI has the potential to provide care in a most effective and efficient way. It is a great equalizer for people around the globe, from every age, gender, ethnicity and location. Simply put, AI could be the roadmap to our promised land of care for all.
Sundararajan Mani is the general manager of clinical oncology forGE Healthcare.He is an accomplished healthcareexecutive with aproven track record of leading global cross functionalteams and delivering new products. He earned his MBA from Marquette University.
August 11, 2022
