Blog

Posts in "big-data"
  • Driving Enterprise-Wide Change by Breaking Down Data Silos and Creating a Data-Driven Culture

    June 8, 2017 Editorial Team in Big DataEnterprise AnalyticsFeaturedMedeAnalytics

    This year’s Big Data & Healthcare Analytics Forum brought together payers, providers, government and academia decision-makers who shared their successes and lessons learned from their transition to value-based care. Of the many thought leaders who participated in the discussion, our client, Soyal Momin, Vice President of Data & Analytics at Presbyterian Healthcare Services (PHS), presented his abstract, “Eliminating Data Silos and Driving ROI.”

    As a large integrated healthcare system consisting of eight hospitals, a statewide health plan and a growing multi-specialty medical group, PHS found it increasingly challenging to oversee its entire business from one integrated view. After investing in an enterprise data warehouse (EDW), PHS continued utilizing several reporting tools from different vendors for each of its business lines that created data silos. For PHS to thrive under the value-based care model, the organization knew they needed to balance their costs, utilization, quality, risk and outcomes. During Soyal’s presentation, he outlined how through their partnership with MedeAnalytics they could strategically differentiate themselves and add value within their integrated data analytics model. To achieve this success, PHS focused on three distinct categories:

    • Creating Value for Key Stakeholders – Creating an integrated, enterprise approach, extends meaningful, actionable insights across PHS and to their business users so they’re able to access content, business rules, benchmarks, best-practice analysis and views.
    • Integrating Payer and Provider Analytics – Through an enterprise approach to analytics, PHS has an integrated overview into their provider groups and health plan. The insights are extended across financial, operational and clinical areas throughout the provider-side of the organization. For the health plan, they can analyze payer data for cost and utilization.
    • Promoting a Data-Driven Culture – Data literacy and data democratization is the foundation for creating a data-driven culture. A key component in creating this was tapping data analysts whose sole job is to gather data and analyze it in a meaningful way to generate results. PHS gave their analysts the appropriate training and mentoring to ensure they were developing a consultative skillset that met the needs of their diverse organization.

    PHS has achieved ROI in its clinical, operational and financial areas within their enterprise. Additionally, PHS recognized operational efficiencies by replacing seven analytics vendors with MedeAnalytics, reducing redundancies and achieving quick wins with business stakeholders. More so, PHS expects to save millions in 2017 by improving collection for Medicaid encounters and increasing business development revenue.

    To learn more, visit our enterprise analytics solutions page or download our white paper here.

    Read More

  • Data Democratization at the Heart of Health Datapalooza 2017

    May 10, 2017 Editorial Team in Big DataClinical Data InfrastructureFeaturedMedeAnalyticsMedicare/Medicaid

    The 8th Annual Health Datapalooza conference in Washington D.C. brought together a variety of data advocates who focused on how to harness the power of big data and put it into the hands of the people who benefit from it most: patients and providers. As part of the two-day event, one of our clients – Ian Morris, Clinical Data Interoperability Project Manager for the State of Mississippi, Division of Medicaid – presented as part of a panel titled “Health Systems Reaching Out to Patients and Providers.” During his presentation, Morris shared Medicaid’s experience of modernizing their Medicaid infrastructure and empowering real-time data sharing across all of Mississippi. In addition, Morris outlined lessons learned around interoperability and the roadmap for Medicaid’s interoperability efforts in years to come.

    After the conference came to an end, we connected with Morris to discuss his experience at the event and other key takeaways. Morris shares his highlights below.

    1. As a first-time attendee and presenter at Health Datapalooza, what intrigued you most about the event?

    It was refreshing to hear the patient perspective. A lot of the time when you attend conferences that focus on data and analytics, you don’t get the rich patient narrative. However, Health Datapalooza took the imperative to put democratization of health data at the heart of the event. Empowering the physician and patient to take control of the data is what we’re all striving for, and that’s where organizations like Medicaid fit into the narrative. You need to understand the value of data first, and that’s where we – people such as interoperability managers – come into play. We translate that value, and once it’s understood by the provider, it can be shared externally with the patient.

    2. What was a best practice that you learned from your peers and what do you hope to see at next year’s conference?

    There were many presentations at the event that delved into the importance of collaborating between multiple state systems (i.e. bridging the broader health and human services, mental health and advocacy groups together) all for the greater good – improving patient outcomes via better data sharing. Such intricate collaboration efforts made me think of the initiatives Medicaid plans to embark on in the future. If there is one take away, it’s that statewide collaboration is key to better data sharing practices. My hope for next year’s conference is to have more speaking panels that touch upon just this, especially as it relates to interoperability efforts overall.

    3. Other post-conference highlights that you’d like to share?

    Health Datapalooza was full of energetic and enthusiastic data leaders. From patient advocates, to vendors to hands-on project managers, conference attendees and speakers embraced each other’s lessons and shared challenges of their own. Serving as a microcosm of what we’re all striving for in healthcare, Health Datapalooza reminds us that the sharing and analysis of data has a purpose – and that is ultimately to improve patient outcomes.

    To read more about how Mississippi Division of Medicaid became the first Medicaid Agency to exchange clinical data summaries with their providers, read their story here. To learn more about how to act on your data and ensure quality, cost-effective care for Medicaid beneficiaries, visit our Provider Access solution here.

    Read More

  • Best Practices for an Enterprise Analytics Strategy

    March 31, 2017 Editorial Team in Big DataFeaturedMedeAnalytics

    The healthcare economy is changing rapidly – from increased consolidation to the rise of consumerism in care, healthcare organizations face a market that requires a holistic understanding of their enterprise (from clinical to claims data) to succeed. To stay competitive and deliver the best quality of care and value, providers should think like payers and payers like providers. As such, strengthening integrated care remains a hurdle for organizations to overcome as they seek to improve clinical quality, reduce operational costs and support care management. To achieve these goals, healthcare systems must be able to have access to data not just within their own organization, but from outside sources – which is often siloed.

    A HFMA Health Care 2020 report on consolidation points out that to succeed in an increasingly competitive marketplace, healthcare organizations are investing in data analytics capabilities to help them understand their patient – and entire business – better. While investing in analytics is an integral key to success, an overall best-practice strategy must be developed to make data actionable. Here are five best practices that should be adopted to initiate an analytics strategy: 

    • Identify enterprise champions – To ensure buy-in from key internal stakeholders, leadership and process changes must occur. Change to the entire organization’s attitude on data governance must come from the top and trickle down to the bottom.  
    • Find value in existing data – As new payment models are adopted, healthcare providers need to design a technical infrastructure that can integrate payer, health system and medical group data within an enterprise healthcare delivery system to create value. Organizations should leverage their core data set and claims data, but also pull in existing ancillary data to have a better understanding of their organization.
    • Create data-driven culture – Establishing an enterprise analytics department ensures that the entire business is standardizing and handling data consistently, but also encourages the new analytics department to champion a holistic approach towards data management. Champions should include representatives across all departments, from clinical to claims teams.
    • Outline and developing manageable goals – Instead of tackling all problems at once, start small. Is the organization focusing on obtaining a streamline, single view of their entire business? By setting a goal with real, manageable next steps, all stakeholders can quickly perceive value in an enterprise initiative.
    • Train, train, train – Repeated trainings and regular communications across the enterprise ensure long-term initiative success. By holding teams accountable, while empowering them with resources to succeed, data sharing efforts across the enterprise are bound to improve.

    Changing goals and evolving organizational structures require players in the healthcare industry to pivot quickly. Whether it’s to meet increasing consumer demands or to better align on value-based initiatives, organizations will need to rely even more on data to achieve their goals. When organizations embrace analytics, and have a go-to data analytics strategy, the procurement and actionable next steps will come naturally.

    To learn more on how to take action with your data, check out our latest whitepaper here. If you are interested in ways we can help you on your analytics journey, learn more about our enterprise analytics options.

    Read More

  • Springing into Spring: Health IT Trends to Look For

    March 30, 2017 Editorial Team in Big DataFeaturedMedeAnalyticsPatient Engagement & Satisfaction

    After a long, cold, winter, the melting snow and warmer weather means spring is finally here. The health IT industry has been busy this past winter with the HIMSS17 conference and the stalling of the GOP health care bill, to name a few. At HIMSS, healthcare leaders were busy discussing machine learning, analytics and population health and sharing new technologies aimed at improving patient care. After the GOP healthcare bill was pulled amid widespread reports that they did not have enough votes to pass it, Republicans say repealing the ACA is back on the agenda, adding to the uncertainty that has faced the industry under the new administration. To kick off the new season, here are a few of the trends that we are keeping an eye out for:

    • Precision Medicine –  Despite concerns regarding the overall effectiveness of interoperability and big data, a survey by NEJM Catalyst Insights Report found that precision medicine data will have a major influence on the industry in the coming years. This new approach to treatment will be used to eliminate medication errors, improve care, treat disease and help provide feedback for physicians.
    • Leveraging Big Data to Solve Population Health Issues - The future of healthcare lies in data but its application is constantly shifting. With the data generated from EHRs and wearables, organizations are faced with growing amounts of useful insight, that in many cases will go unleveraged due to silos or a lack of analytics tools. Throughout the winter, we saw how communities are leveraging data to tackle the opioid crisis, pathologists are using it to detect breast cancer and providers are using it to reduce waste. We will be keeping an eye out to see how organizations will continue to leverage big data to draw insights and uncover new tools.

      Read More

    • Analytics Are Nothing Without Action

      March 16, 2017 Editorial Team in Big DataFeaturedMedeAnalytics

      Although the investment in health IT innovations continue to rise, with 56 percent of providers investing in IT this year, there still remains a disconnect on how to best leverage the data and insights that organizations have at their disposal. With a new healthcare economy focused on value, provider consolidation, consumers and changing reimbursement models, healthcare organizations need a holistic, enterprise-wide view of their business. They also need valuable data insights to tap into cost savings opportunities.

      In this week's post, we continue to offer best practices that healthcare organizations can utilize to make the most of their analytics’ investments and turn those insights into action. Here are three key strategies that can make a significant impact on an organization’s financial health:

      • Offer Self-Service Access to Business Users: In many cases, organizations have invested in creating an enterprise data warehouse, but the adoption of the platform is often delayed due to IT bottlenecks in reporting and analysis. By empowering business users with the ability to perform their own analysis to identify the root causes of trends, the speed from insight to action will increase.
      • Find New Value in Existing Claims and Billing Data: Most data warehouses focus on aggregating clinical data from EMRs, but many healthcare organizations fail to recognize the potential in claims and billing data. Most data models are built on this type of data, so their value should not be underestimated. Organizations should also recognize that most risk models and clinical measures, including CMS quality measures and HEDIS measures, are based largely on this data. By using this insight, organizations can better prepare for the changing risk models and clinical measures.

      • Learn to Work with Payer Data: As new payment models blur the lines between providers and payers, leveraging payer data has never been so important. By creating a data infrastructure and training your staff to manage and analyze payer data, organizations can develop analytics for utilization measures and per member per month (PMPM) cost. These insights can help support new business models and uncover nuances to create added value across the enterprise.

        To obtain more strategies, check out our latest whitepaper, Harnessing Enterprise Data Analytics for the New Healthcare Economy, here.  Learn more about integrated analytics for the healthcare enterprise here.

      Read More