The healthcare industry stands on the precipice of change, facing a series of interconnected challenges that strain the entire healthcare system. These existential pressures include:
- Rising rates of chronic/co-morbid conditions
- Resource constraints including clinical staff shortages
- Surging demand for services
- Cost pressures and overall decrease in reimbursement models
As in other industries, healthcare decision-makers and thought leaders have used this adversity to fuel innovation. In response to these challenges, hospitals and other healthcare organizations are adopting new technologies at a fever pitch.
Of the emerging or rapidly evolving technologies that have the potential to help healthcare organizations navigate these challenges, artificial intelligence for doctors is the most promising.
However, before organizational leaders can fully embrace artificial intelligence in healthcare, they must tackle several key AI ethics issues to enhance the patient experience, pave the way for improved outcomes, address staffing shortages, decrease the workload on clinicians, and protect business continuity.
The State of Healthcare AI
By late 2021, the global healthcare market totaled approximately $11 billion. However, only a meager 9% of organizations have used AI modeling technologies for five or more years, and a mere one in five healthcare organizations used AI technologies in the last two years. The overwhelming majority of healthcare institutions are either evaluating use cases for AI or not actively considering the technology.
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Despite AI’s relatively slow adoption rate in healthcare, the industry bears a critical void that only artificial intelligence and machine learning technologies can fill. Hospitals and healthcare organizations collect massive amounts of data but lack the resources to process and use that information efficiently.
By adopting AI for doctors, healthcare leaders can tap into the power of data analytics, gaining access to robust insights about their organization that can guide decision-making processes, stabilize cash flow, and facilitate business growth.
Looking Beyond CRM: Artificial Intelligence for Doctors and other Allied Health Providers
Customer relationship management (CRM) is a critical component of the care process, as patients are ultimately customers. Healthcare leaders recognize the vital role that CRM plays in the overall financial health of a hospital and its impact on the patient experience. As such, some decision-makers view CRM optimization as the primary use case for AI in healthcare.
While a great way to deploy artificial intelligence, doctors can use AI to profoundly impact patient outcomes, quality and efficiency of care, and a hospital’s reputation.
Leading-edge AI solutions can analyze massive amounts of patient data and understand context, allowing the technology to make care recommendations and customize how EHR (electronic health record) data is presented to providers. In turn, physicians can focus on critical areas of concern, remain aware of insurance requirements, and deliver better care.
AI Ethics Concerns in Healthcare
Artificial intelligence in healthcare can directly and indirectly impact patient care experiences and outcomes. Before hospital leaders can tap into the benefits of artificial intelligence, they must overcome four AI ethics issues.
Hospitals and other providers must obtain informed consent from patients to handle their data with AI technology. Consent is and will remain a cornerstone of all healthcare activities, even in the age of artificial intelligence. Failing to obtain consent could expose a hospital to significant civil liabilities.
HIPAA and other regulatory legislations still apply to healthcare data, whether it is processed and analyzed by human personnel or artificial intelligence technologies. As such, healthcare organizations must ensure that they are transparent about their use of AI while simultaneously verifying that they are proactively working to protect patient data.
3. Algorithmic Biases
Artificial intelligence technologies have the potential to embed social or human biases into algorithmic processes. The technology can then apply these biases at scale.
These biases are not an intrinsic part of the algorithms; instead, the algorithms can be influenced by flawed data collection or submission practices. Therefore, healthcare organizations must ensure that they adhere to fair and equitable data collection practices so that they do not inadvertently corrupt the unbiased nature of AI algorithms.
4. Data Privacy
Healthcare organizations must consider data privacy as a critical AI ethics issue. Data privacy has been pushed to the forefront of technology ethics conversations in light of several data privacy laws, including the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPR).
AI ethics should not discourage healthcare leaders from investing in artificial intelligence technologies. As long as these considerations are appropriately addressed before the technology is implemented, AI software is a valuable asset to modern healthcare organizations.