Healthcare
Leveraging AI to alleviate healthcare administrative burdens
According to research, the administrative burden in healthcare contributes to one-quarter of all expenses. Additionally, over 90% of healthcare workers in the US experience burnout due to excessive administration.
Leveraging AI streamlines and automates the extensive administrative tasks in healthcare settings. This approach helps reduce the time and effort healthcare professionals spend on paperwork, scheduling, billing, and regulatory compliance, allowing them to focus more on patient care.
Additionally, AI-driven data analytics can help in managing patient records, predicting staffing needs, and optimising resource allocation. By integrating AI solutions, healthcare organisations can enhance productivity, reduce costs, and improve overall patient outcomes.
Let's look at the importance of data interoperability and the use of AI in healthcare administrative applications in detail.
Data interoperability in the healthcare industry
An interoperability framework is the foundation of simplifying healthcare administration and is one of the requirements of the Health Insurance Portability and Accountability Act 1996 (HIPAA).
Interoperability is important in healthcare because patients often receive treatment from various clinicians across different healthcare systems and insurance networks. Ensuring a seamless flow of patient data is crucial for providing consistent and coordinated care. Healthcare organisations can achieve interoperability at various levels:
Level 1 (Foundational)
Foundational interoperability allows basic data exchange between systems without requiring data interpretation. For example, a nurse must manually enter into the EHR a PDF file of a patient's history sent through a hospital portal.
Level 2 (Structural)
Structural interoperability, also known as syntactic or technical interoperability, defines the format of data exchange between systems for clear context and interpretation, ensuring data is preserved and unaltered.
Level 3 (Semantic)
Semantic interoperability allows systems with different data structures to exchange and interpret information seamlessly. This interoperability ensures standardised, codified data, creating a common vocabulary for accurate machine-to-machine communication. It optimises patient and organisational outcomes by enabling full data exchange and use across disparate systems.
Level 4 (Organisational)
This level includes governance, policy, social, legal, and organisational considerations to ensure secure and seamless data communication and use within and between entities.
An interoperability framework combined with artificial intelligence (AI) brings a transformation in healthcare administrative burden.
Administrative applications of AI in healthcare
Healthcare AI builds upon machine learning that can process and analyse large amounts of diagnostic, patient care, prescription, and treatment data. The AI model uses this data to predict medical outcomes and identify subtle symptoms that a human can miss.
Additionally, AI automates and streamlines mundane tasks such as filling out OPD forms, diagnostic tests, and discharge paperwork, ensuring accurate data entry and consistent information across systems. AI can also automate lengthy and tedious health insurance claims processing and appointment scheduling. This can give the administrative staff more time to invest in strategic tasks that require human analysis.
By integrating AI, medical professionals gain greater autonomy over workflows and can reduce errors in health records, medical image reading, and understanding test results. This results in better patient care in an efficient budget.
AI challenges in healthcare
While AI and interoperability present significant benefits in healthcare, they come with ethical, regulatory, and patient safety concerns that one must address. Common challenges of AI in healthcare include –
- Patient data privacy and security.
- Training the AI model with accurate data to recognise patterns.
- Integrating AI with existing systems and machines within the healthcare system.
- Gaining the trust of physicians.
- Complying with federal laws.
The AI model must refer to accurate data and identify patterns to provide accurate and tailored treatment recommendations based on the diagnosis. Additionally, the service provider must understand the existing systems within the healthcare system to integrate an AI model. All these factors are essential for patient safety and service quality.
How can Infosys BPM help healthcare organisations use AI?
Infosys BPM healthcare services leverage deep knowledge of the healthcare industry along with a flexible operating model and integrated IT-BPM solutions to address evolving needs. Its solutions aim to transform operations, enhance performance, and standardise processes while reducing costs.
Explore Infosys BPM healthcare BPO services.
