The COVID-19 demonstrated the harsh consequences of the healthcare sector’s slow adoption of data-driven decision-making technology. As a record number of people became ill, demand for personal protective equipment (PPE), ventilators, and other supplies increased, and healthcare professionals struggled to obtain timely, accurate information about critical supplies, recommended treatment practises, and local surge specifics.
To say that the pandemic highlighted the value of data in healthcare would be an understatement. For more than a decade, the industry has been gradually automating its business processes in order to increase efficiency and reduce waste. The transition to a value-based care model has increased the importance of data in assisting healthcare to finally understand the true cost of providing care, including supply costs and expected patient outcomes.
Accelerating the adoption and integration of more modern, data-driven technologies, as well as leveraging the power and flexibility of the cloud, is critical to addressing the healthcare supply chain’s complex challenges as the industry seeks financial recovery and future resiliency.
Visibility and Transparency: The Way Forward
The need to improve efficiency, reduce costs, and understand the true cost of care has driven recent investments in enterprise resource planning (ERP) systems, digital supply chains, and electronic health records (EHR). The next step in the technological transformation of healthcare is the fusion of these IT systems to support a clinically integrated supply chain. When organisations are able to incorporate precise, data-driven information into a hospital’s systems, such as:
- Establishing a centralized, trustworthy information source to better manage the use of drugs and medical devices while providing care.
- The ability to rely on evidence when making decisions about what to buy and how to use products.
- Greater cost and efficacy flexibility for medical products
The exchange of clean, accurate data that identifies cost, quality, and outcome factors is the foundation of clinically integrated supply chains. In contrast, healthcare has a data problem: despite producing massive amounts of data, the industry struggles to gain the knowledge needed to make sound decisions.
The good news is that this concern is surmountable. The first step is for IT teams at provider organisations to create a modern data strategy that integrates data and ensures it is accurate, clean, and easily flows across systems. Four pillars support a modern data strategy:
- Data Platform: Real-time streaming, which enables seamless data ingress and egress across systems, is critical for timely data delivery for analytics and decision-making.
- Data Maturity: In order to provide answers to fundamental questions about overall business performance or patient outcomes, the integrity and quality of data surrounding key entities such as patients, products, providers, suppliers, procedures, and facilities must be improved. Measuring the fill rate of critical data attributes and the match rate of duplicate entities is required to establish data maturity and solid master data management practises.
- Governance: Once data has been clearly defined, organisations should implement a data governance programme to ensure the data’s ongoing quality and integrity. Many stakeholders place a high value on a single healthcare entity, such as a procedure and its associated procedure codes. Creating a data governance team can help decide how data assets can be used and accessed, avoiding confusion and duplication of effort.
- Security: In the healthcare industry, sensitive patient information is frequently present in data. By incorporating security into a modern data architecture, a company can balance data access to various stakeholders while prioritising security and data rights. Aside from general concerns about network security and other aspects of an established security programme, there are a number of other critical factors to consider. Maintaining the immutability of the original data, implementing role-based access control for the data as it moves through an organization’s data pipeline, and documenting the changes the data undergoes during transformation are all critical issues.
Supply chain teams can better understand product utilisation and pinpoint the products that produce the best results for patient outcomes with the help of clean, clinically aligned data.
Cloud Tech is Essential for Improving Operational Efficiency
The healthcare sector has been slow to adopt cloud technologies due to the sensitive nature of the data that flows through its systems. However, healthcare CIOs are starting to acknowledge the necessity of adjusting to the digital age.
For many service providers with outdated, and occasionally even proprietary, IT infrastructures and restricted IT budgets, the cloud offers better scalability and a lower cost of maintenance. As a result, IT resources can be allocated to more challenging tasks like system integrations with internal and external systems and data sources.
By enabling real-time integration with other cloud-based systems such as electronic health records and supply chain management, cloud-based ERP systems will enable the development of clinically integrated supply chains. Furthermore, using standards-based integration between different cloud-based systems reduces and accelerates cost and integration time. The speed with which businesses adopt cloud-based ERPs will determine their success in the race to fortify their supply chains.
Organizations must prepare for success by laying a solid data foundation and ensuring that all of their operational processes are fully automated and interoperable, allowing the data feeding the ERP system to support more informed clinical and operational decisions.
An AI-Driven Approach to Patient and Treatment Analysis
Data preparation is critical to the accuracy of the analysis’ findings when using AI for data analysis. Organizations in the life sciences must collect massive amounts of data while also ensuring that it is of high quality and ready for analysis. To make informed decisions about matching patients with clinical trials and new treatments, diverse data from operations and research around the world must be gathered. If the data is incomplete or incorrect, any insights gained from AI solutions will be dangerous, useless, or both.
Data management is one of the most difficult aspects of preparing patient and treatment data for analysis. The quality and completeness of data gathered from various sources does not always match. It must be standardised and cleaned prior to analysis. For example, data collected from a clinical trial, where data is regularly recorded on digital media, may be more thorough than data collected from an HCP who took handwritten notes. The problem is exacerbated by the fact that each country and region have its own set of regulatory standards that govern patient and consumer data. To comply with GDPR requirements, data in Europe typically has to remain on European servers; in China, no healthcare data can cross borders; and in the United States, data is pulled from any relevant source and compiled for analysis, much like in the wild west.
Regardless of these challenges, the advantages of creating a single source of truth by collecting and cleaning healthcare data from all available sources are undeniable.
Matching Clinical Trials to Patients Using a More Strategic Approach
Tracking patient journeys throughout the entire cycle was a significant challenge prior to the adoption of cloud-based solutions and AI-driven analysis in the life sciences industry. Due to siloed data and operations, only fragments of information about a patient’s illness, medications, reactions, experience, and outcomes were available.
Life science organisations can use AI-driven technology solutions to combine patient and treatment data to create a comprehensive patient profile. This has two benefits: data can be used to bring drugs to market more quickly for patients who need treatment for critical and deteriorating illnesses, and patients can be more effectively and safely matched with clinical trials that have a high potential to treat their illness.
In Conclusion
When assessing the state of healthcare in the future, data will be the primary consideration. Healthcare systems must review their IT strategies as the healthcare industry uses data to balance cost, quality, outcomes, and finances. Data are increasingly being used in health-care decision-making. Healthcare organisations must invest in the necessary technology to provide this analysis in order to maintain their current level of productivity and efficiency. Until this is completed, we will be unable to use data to identify the best products, services, and strategies for improving patient outcomes and building a more robust and long-lasting healthcare supply chain.