But even as healthcare organizations continue leveraging the latest tech tools – such as AI, analytics, cloud computing, cybersecurity, and connectivity technologies – they are bound to face challenges. To better understand these challenges, our analytics team has spent the last few months delving into the Google search data of thousands of healthcare companies to uncover the most pressing issues they face.
Striking a Balance: Fair Pricing and Market Competition
But an analytical tool is only as good as the data you feed into it. As such, healthcare organizations are increasingly seeking additional data points to inform their analytical pricing models.
For instance, more in-depth patient data can allow healthcare organizations to implement differential pricing models allowing a provider to set different prices for the same service for different customers. Real-time pricing fluctuations are another useful data point, as they can instantly alert a healthcare provider to pricing changes from their competitors. This allows providers to ensure their own pricing for the same service remains competitive. Other vital data points include sales volume data and patient churn rates.
The takeaway from this is that healthcare organizations are seeking more advanced health service pricing strategies and technologies. Their search focuses primarily on augmented consumer data, which reveals a consumer's digital footprint across devices and channels. By analyzing this data, companies can gain real-time insights into pricing scenarios and predict customer churn, with 52 percent of organizations stating that data analytics help improve pricing decisions.
Mastering Enterprise Data Management
Enterprise data management involves collecting, organizing, accessing, and transforming data into consumable information. However, effective data management and governance remain a top challenge for many organizations. According to a Gartner study, only 44 percent of data and analytics (D&A) leaders consider their teams effective in providing value to their organization. With that in mind, healthcare organizations must be clear about what they hope to achieve in their digital transformation when developing efficient enterprise data management and governance systems.
For example, any transition you make from physical medical documents to electronic health records (EHR) is bound to run into hurdles unless you prepare carefully. Some of these hurdles can include an overwhelming EHR data burden on a limited workforce and excessive data monitoring once the transition is complete.
Another major data management challenge includes implementing and maintaining strict data access controls for cybersecurity purposes. Such access controls are bound to cause pushback from staff who view them as a nuisance to work with, necessitating a balanced approach to data security that doesn’t cause undue friction with staff.
Other data governance challenges include the need to classify the sensitivity level of each data point and identify entry errors and missing data. Our research indicates that AI-driven tools, such as data gathering and classification algorithms, can greatly enhance enterprise data management in health care. In fact, we found that 45 percent of healthcare organizations believe AI will significantly impact their data management processes by 2025.
All organizations that are in the process of digitizing their operations are certain to encounter profitability challenges, particularly when it comes to implementing highly complex and expensive digital ecosystems. For healthcare organizations, these profitability challenges can be especially severe when faced with increasing market competition and economic uncertainty.
For instance, health organizations may be tempted to upgrade their tech infrastructure piecemeal for budgetary reasons. However, this can result in a hodgepodge of new and legacy systems that are likely to encounter compatibility issues that drive up the costs and complexity of maintenance. Other complexity issues can arise from a lack of experience with the latest tech tools and processes, resulting in lost time and poor investments.
The good news, though is that more health care organizations are starting to recognize the need for a more holistic and pre-planned approach to digitization. They’ve also begun exploring new tech tools that can help them reduce complexity and bolster profitability, such as smart analytics, data forensics, automatic image annotation, and data platforms. While such an approach to digitization can require great effort and planning, our experience has shown that a holistic approach is the right way.
A common thread in these digitization challenges is the siloed nature of healthcare data. The key is establishing a foundation for a scalable and efficient data pipeline. With this foundation, healthcare organizations can adopt advanced analytics capabilities for pricing, data management, and profitability management, ultimately driving digital transformation success.
Looking to overcome the challenges of digital transformation in the healthcare industry? Learn how Mactores can help you!