How Self-Service Analytics Can Elevate Organizational Data in Healthcare

Data innovation leveraging analytics and AI is essential to advancing the strategy and operations of any business. In healthcare, however, it is imperative—quite literally, lives are on the line. As digitization transforms the consumer/patient experience, disrupting healthcare as it has done in countless other industries, healthcare organizations can leverage this wave to streamline operations, enhance patient and workforce experiences, accelerate research and transform care. 

Self-service analytics, particularly exploratory analytics, is a fundamental capability in enhancing accessibility and fostering data innovation within a healthcare system, and it contributes to the overall mission of improving people’s health and well-being. 

Self-service analytics enables healthcare professionals to access, analyze and visualize data independently. The tools, unlike traditional analytics that tend to require technical expertise, aim to be user-friendly and accessible to non-technical users. This approach democratizes data analysis, providing professionals across an organization like clinicians, researchers, administrators, staff and leaders with the ability to make data-driven decisions without extensive reliance on data analysts or additional technical support. 

Common features of self-service analytics include an intuitive user interface customized for a specific use case, or user type, with guided navigation. This provides professionals with easy access to diverse data sources like patient, staffing, supply and financial information, uncovering new reporting opportunities that can leverage advanced analytics with ad-hoc analysis. 

While the impact of self-service analytics is far-reaching, there are two notable applications in healthcare that can drive significant results worth highlighting: 

  1. Optimizing Patient Flow

    Self-service analytics allows clinicians and administrators to analyze near-time and predictive data on critical aspects like patient admissions, discharges, throughput and workforce staffing. This analysis enables staff to identify bottlenecks and inefficiencies in patient movement throughout the facility, including trends in admission rates, bed availability and average length of stay. At its core, the ability to predict problems enables healthcare professionals to proactively avoid them.These improvements allow an organization to be proactive rather than reactive by optimizing schedules for surgeries and other procedures, ensuring the effective utilization of resources including operating rooms and staff. This significantly reduces costs and improves patient outcomes, and it also increases overall patient satisfaction. Internally, this can create an environment that is more stable and predictable, leading to improvements in the workforce experience and minimizing the potential for employee burnout.

  2. Quality Improvement

    With the implementation of self-service analytics tools, clinicians are able to identify real-time trends, patterns and improvement opportunities by analyzing data around patient outcomes, treatment efficacy and post-operative recovery rates. Access to this data in real-time allows them to quickly assess the effectiveness of different medical procedures and interventions. These tools allow an organization to monitor critical key performance indicators including hospital readmission rates, patient satisfaction scores and clinical guideline compliance.Through consistent analysis in these areas, providers can implement best practices more effectively, including evidence-based improvements and tailored treatments that meet individual patient needs. This creates a culture of consistent monitoring and analysis of healthcare data, allowing providers to continuously implement quality improvements that enhance patient care and safety, and, ultimately, drive better health outcomes.  

Additional Use Cases 

There are several use cases for self-service analytics tools, with additional opportunities in market planning, financial planning, workforce management, patient recruiting and facilities management, just to name a few. Major software vendors like those providing EHR, ERP and CRM systems offer a range of tools for these purposes. However, it’s crucial not to rely solely on these vendors’ offerings as your primary data platforms, especially as more third-party options emerge. Your organization’s strategic and operational goals should guide your prioritization of which use cases to tackle initially.  

Items to Consider  

While self-service analytics tools offer significant benefits, there are some items to consider. The careful and strategic implementation of any new tools or processes is essential. This includes identifying potential issues in advance and proactively developing mitigation strategies to address them. Some common issues organizations have experienced, and potential solutions to address them, include:  

  • Data Quality: With self-service tools, there is no longer a data analyst or data scientist between the data and end-user that can factor data quality issues into the final analysis. Organizations will need to prioritize investing in improving their data quality through the implementation of data governance frameworks and documented data cleansing processes to maintain data integrity. Implementing tools like data quality monitoring mechanisms can help identify and rectify data anomalies proactively. If this kind of investment, whether through new tools, systems or internal teams, is not possible, it will increase the complexity of the self-service tools and will require a heightened focus of employee education.
  • Data Fluency: Learning how to use a new tool or system can be relatively easy, whereas learning how to interpret and tell stories with data can be more complicated. With the implementation of data literacy initiatives like ongoing training programs, workshops or resources that focus on data storytelling and visualization, organizations can establish a culture of proficiency on how to frame analysis objectives, how to develop a deep understanding of the data and how to effectively interpret and communicate the results. Without this framework or employee investment, it can lead to incorrect conclusions of data and potentially harmful decisions—which can be especially detrimental in a field like healthcare.
  • Operational Integration: The successful adoption of self-service analytics relies on seamless alignment with existing processes and workflows. Without an adequate level of support within the existing operations, leveraging data from self-service analytics won’t be possible. Organizations should involve stakeholders throughout the organization during the implementation process, ensuring communication, buy-in and support at all levels. Establishing a tiger team to address specific issues creates clear communication channels and feedback loops that facilitate collaboration and foster a culture of data-driven decision-making.
  • Vendor Dependence: Relying on a specific vendor’s tool can negatively impact organizational flexibility and create challenges for future integrations or tool adoptions. To address this, organizations should consider the process of how they implement solutions at the beginning of the process. Creating a single source of truth (e.g. data mart) establishes a foundation for strategic implementations, making the transition to additional products or tools easier and quicker. In working with specific vendors, it is important to negotiate pricing in advance to receive favorable rates and avoid committing to upfront payments of more than one to two years out.   

Self-Service Analytics Empowers Data Innovation 

Self-service analytics can be a game-changer in how healthcare organizations harness their data to uncover innovative and transformative opportunities. By democratizing data access, prioritizing employee education and carefully considering potential issues—while also proactively identifying mitigation strategies—organizations can accelerate their data innovation journey towards data-driven decision-making and achieve greater success. 

About Pivot Point Consulting’s Analytics & AI Practice 

With the vastness of data in healthcare today, clinical and business leaders are challenged to understand, manage and capitalize on high-value data to drive effective clinical and business decision-making while managing risk. To thrive, businesses need to adapt quickly, form connections with customers, and predict what’s next without missing a beat.  

That’s where Pivot Point Consulting’s analytics strategists, artificial intelligence practitioners and data scientists come in. We help healthcare providers and payers turn data into insights, and insights into outcomes – giving you a critical edge to capture and aggregate your data, organize it to meet your needs and create healthcare data analytics to enable informed clinical and business decision making.  

To learn more about how self-service analytics can elevate your data, contact us today: