WynTec is establishing a unique healthcare consortium model, that allows models, analytics, population and measure, key performance indicators, data discovery and data mining algorithms and quality rules to be shared across multiple customer bases.  At all times the unique customer data will never be shared or lifted from the security of their firewalls. But the knowledge established in WynTec’s heavy bag of healthcare data services will be made available to participating consortium partners.

The consortium partners will be direct customers in the health care, health delivery, health plans, managed care, back-office partnerships and value-added vendors that offer specialized products and services in our domain. WynTec does not believe that the smorgasbord of data products can be effectively built or managed by one organization but a teaming approach among peers and experts alike creates the most synergetic offering.  

Problems Healthcare Organization Face – Download Whitepaper PDF

Healthcare Organizations are constantly running to improve their data management platforms. Over the last decade data projects have competed for attention. Initiatives like HIPAA, Electronic Health Records/Electronic Medical Records (EHR/EMR), Privacy Health Information (PHI), ICD-10, Affordable Care Act (ACA), Affordable Care Organization (ACO), Exchange and Integration have kept the services arm quite busy.

Many of the progressive healthcare organizations have embarked on the mission to be data-driven organization. There is direct correlation for improved patient care and satisfaction, process efficiencies and enhanced services by assessing knowledge from accurately and timely information.

Becoming a data driven healthcare organization is an honorable goal as information is a viable healthcare asset. However, the practice of collecting the relevant clinical, financial, patient, demographic, historic and utilization data funneled through an enterprise analytical engine in a clinical care setting has been a slippery slope with many unaccountable roadblocks:

The Market

Recent survey from KPMG announced that only 10% of hospitals and clinics believe their organizations are using data and analytics at their highest potential. The opportunity to help the other 90% to turn their data into insightful information is eminent. Corporate executives and data managers all agree that information is a key asset to their organization.  To seek value in this information, the data must be collected, organized and processed as an enterprise asset. But, in many cases, their hands are tied as the cost to develop data warehouse, data marts and enterprise reporting engines far exceeds the budgets, focus and skill levels of most hospitals.

In today’s market, enterprise class data warehouses are not prevalent in hospitals. They are buried with operational issues pertaining to managing EMRs and ancillary systems. Arm wrestling with messaging data between internal and external systems; dealing with interoperability and data latency issues, HIPAA, PHI, state reporting, exchange, and research (to name a few). These factors place a drain on their data resources hence healthcare organizations limit their analytics capabilities to operational reporting.

Healthcare organization attempt to learn from the marketplace and are often overwhelmed by the overload of vendor hardware, vendor software and vendor promises.  Recent analysis found over thirty three (33) commercially available reporting tools that tout to be best-of-breed. There are over a dozen different database technologies, and ½ a dozen data movement tools.  All claiming to be the right choice in the analytics stack. No wonder, healthcare organizations are confused as they are swamped with too much market information in a rapid changing Business Intelligence ecosystem.

Silo Reporting

Some establish departmental teams to generate operational silos to mimic reporting data extracted from the heterogeneous EMR and ancillary systems (such as LABs, PAX, Claims, AR, GL, HR and Supply Chain). In many cases these reporting silos do not communication with each other and islands of disconnected reporting structures are in place. The islands of data that are not connected to each other offers very little information insight across organizations. Simply put, without integrated information you cannot measure what you do not collect.

Healthcare organizations have even reached out to the EMR vendors to provide reporting services. This reach only grasped air as the EMR vendors are not organized to offer analytics services across disparate systems. These vendors expertise is building Online Transaction Processing (OLTP) systems and, at best, are service providers for operational data from their own EMR. Healthcare organizations have reaped little enterprise value pursuing the EMR vendors to assist with their enterprise analytics.    

Recently, many of the progressive healthcare organizations have embarked on the mission to be data-driven organization. There is direct correlation for improved patient satisfaction, process efficiencies and enhanced services by offering accurately and timely information. The enterprise data will need to be dismantled from the many source systems, both internal and external, brought into central models and structured appropriately for meaningful insight and actionable analytics.

Mergers and Acquisitions

The latest drive of many of the larger facilities is the aggressive push to grow business by agglomerating through mergers and acquisitions (M&A). During the integration period, the efforts are initially focused on aligning the products, resource and services under one company banner. The general practice is to leave the data in disparate EMRs and silo data stores until the services are combined.  

Post integration, the newly merged organizations center their operational data into a consolidated EMR but they tend to leave the ancillary, historic and reporting data in legacy data stores.  Hence creating more silo databases. To fully integrate the data, the merged organizations must extract reporting data from the operational, legacy and historic data into a central environment. The data must be non volatile, historic, transparent and of sufficient quality.

Attempts to Remedy

Some achieve this centralization by moving towards enterprise data warehouse, federated reporting or virtualization (dynamically extrapolate the data from the source systems). However, only the enterprise data warehouse framework truly captures the history and time variances necessary for longitudinal and latitudinal reporting. The visualization path is an effective discovery method and ideal for prototyping but not suitable for longitudinal and latitudinal analytics.  

Traditional data collection processes, data integration and data warehousing initiatives require a lot of investment, coordination, governance and specialized resources. Many initiatives have shown little success due to the lack of knowledge, minimum best practices and weak governance in terms of commitment and process.  

Some of the bigger vendors (such as Oracle, Teradata, IBM and SAP) try to fill this empty space, by delivering vendor products and generic healthcare data models. These generic healthcare models are sufficient for basic reporting needs, but do not efficiently handle unique customer requirements. These models address typical healthcare population, diseases, measures and utilization reporting.  But fall short when the data mining and discovery process is outside of the general definitions. Soon, the intelligent healthcare organizations, that leverage information to drive their business, will thirst for more information than what these generic industry models can offer.

To move to an enterprise model, larger organizations are recruiting executives to help position their data as strategic insight for business decisions. New characters are being included in the executive ranks; such roles as Chief Data Officer (CDO), Chief Knowledge Officer (CKO) and Chief Research Officer (CRO) are emerging. The executive mission is to place meaningful enterprise information in the hands of the decision makers. With this information the organization is more effective in terms of delivery, performance and patient satisfaction.

However, the practice of collecting the relevant clinical, financial, patient, demographic, historic and utilization data funneled through a central data repository in a clinical care setting has been a slippery slope with many unaccountable roadblocks:

• Time is also of the essence for proactive decision making. An important criterion is the time it takes to deliver the enterprise information to decision makers. Many data extraction and data assembly processes of simulating the information into meaningful structures may take many weeks.
• To exasperate the time latency issue, and as a result of the manual efforts involved in preparing the data, the information delivered tends to be non transparent, lacks sufficient quality and often volatile as history is not captured and reproduction of the information is not feasible.

These organization, soon lose their confidence and competitive edge as decisions are made by looking at their data in the rear-vision mirror.  Even worse, making bad decisions based on erroneous data is very costly for healthcare organizations. In some cases the information is not even available and the executives are maneuvering their facilities in the dark. Because of this, the often used phrase “garbage in is garbage out” actually becomes “garbage in is gospel out”. This does not lend well for effective patient care.  

It is understandable why most healthcare organizations have not been successful with mining their enterprise data. Especially when one considers the lack of technology, excessive cost and typical time it takes to build an enterprise data warehouse. The success quotient is aggravated by complex governance, restrictive budgets, competing politics, lack of metadata and ontology plus the fear of change. Healthcare organizations seek partners that offer vendor agnostic data frameworks; that are easy to understand and satisfy the uniqueness of the customer. The technology partner must also understand the practice of healthcare organizations.

The presented data frameworks must be domain specific; including industry knowledge blended with robust technical effectiveness. That is, it must include best-of-class architecture, optimum processes, iterative development lifecycle, use case driven designs, agile deployment, feasible time-frames, metadata and ontology navigated and delivery based on healthcare business drivers.

WynTec addresses these issues – Download Whitepaper

WynTec has been delivering data assets for healthcare and managed care organizations for over two decades; our services are specialized, expedient and affordable. Our goal is to help breakdown the technology barriers and assist business owners in exploring the wealth of healthcare data, manage the measures of interest and mine data patterns to improve healthcare informatics. The following write-up identifies some of the underlying issues why enterprise data frameworks, for healthcare organizations, fall short and recommend some approaches to achieve success.

WynTec has established proven products and services tailored for healthcare and managed care data initiatives. WynTec adopts a pragmatic and vendor agnostic approach in architecting and designing analytical frameworks. Our goal is to offer true value by architecting and designing models to suit the unique customer needs. Often this design includes a hybrid integration of best-practice designs, tools and services.

WynTec has helped many customers by extrapolating insight into their enterprise web of information. Information is extracted, collected, organized and made available for reporting. The framework includes data organized to facilitate business intelligence, dashboards, and self-service reporting. Suitable for clinical, operational, quality, financial, and payer needs. These models and services are offered through subscription and delivery based pricing models. The following are some of the flexible data frameworks:  

• For each customer and in their facility, we will build custom Data Integration platform that streamlines and automates the data acquisition responsibility for landing and staging analytical data into a central environment.
• The Data Collection Services offered by A2B Data™ will automate this design as it securely extracts any source data irrespective of database architecture or data type.
• The Data Integration architecture is extensible and reusable to feed source data to Enterprise Data Warehouse, Data Migrations, Data Marts and Reporting Structures. The Data Collection Services sets up and moves massive amount of data extracted from disparate source systems in minimum time.
• WynTec Services includes the designs to automate the extraction of data from disparate source systems, found on remote and local servers, that consistently loads a central framework.
• A2B Data™ manages this component of extracting, migrating and loading real-time, event triggered or scheduled data. The latency of data movement can be near real-time (HL7 or X12) or triggered by time or event. The analytics objects are fed by agile transformation utilities that move latent and near real-time data. This offers a unique edge over static reporting engines, where decision makers obtain enterprise insight early in the process. In fact within minutes of the episode or triggered segment.  
• The design of the central framework includes a staging area or Data Lake that sources and lands all the source data, history is preserved, data is time variant and of quality. A2B Data™ is codeless and utilizes metadata driven design patterns to perform flexible change data capture (CDC) methods from any flat file or source database.
• Industry specific models are available that collects and “masters” the business data into common dimensions and facts. This is an important factor to be able to collect multiple EMR data, across multiple source systems, into a common integrated platform. Common “mastered” dimensions of data are organized in meaningful classes (e.g. Appointment, Encounter, Orders, Labs, Meds, Procedures, Charges, Budgets, Membership, Claims, etc.). These data classes are connected through subject specific relationship or late binding agile queries.
• The framework includes knowledge based and user driven rules engine to identify data quality, population segments and flexible measures. Event driven analytics is a critical component to trigger dynamic workflows. Based on the knowledge extracted in the data near real-time. The event may be triggered by a HL7 segment or data validation checks built as rules. These triggers will create reporting objects for population and meaningful measures.
• The final layer is the end user analytics layer that deploys iterative objects suitable for reporting structures. The presentation layer is driven by flexible models to allow multiple reporting tools to connect to the data. The device driven reporting tools can now access the analytical environment and offer care givers near real-time information.

WynTec offers its data services as a consortium partnership that allows multiple regional clinics, hospitals, payers or managed care organizations to participate in the shared development and pay-as-use business model. The consortium model allows advanced nursing, provider and operation analytics at an affordable pay-as-use lease model.

The delivered product will include the following key components:

• Architect the enterprise hardware and software, enterprise data layers and governance processes for product definitions, metadata, data quality and business glossary
• Extending the A2B Data ™ Services to feed the central staged data repository
  • Extract data remotely from external systems or the cloud
  • Subscribe to the electronic message brokers to extract  HL7 messages
  • Leverage the latent data extracted from the EMRs and auxiliary systems.
• Customize WynTec’s healthcare logical models to load the necessary subject areas, conformed dimensions and facts.  All these objects will be mastered to allow for cross-data integration.
• Develop useful populations and measures for the use case needs, driven by the rules engine.
• Establish the reporting, summary and aggregated data marts.
• Integrate the presentation and reporting plug-ins.
• Deliver the data to the customer to develop dashboards and self-service analytics.

Wyntec Analytics Service stretches past the typical data warehouse and reporting engines and delivers true business insight by applying knowledge edits developed by industry experts.  A key component in the technology stack, and as a result of the consortium partnership, is extending the rules engine to control the management of regular time-sensitive pattern searches.  The rules engines will mine for patterns in data that fall in the following categories:

• Include design and recommendations for data quality checks
• Define and identify population and measures of interest
• Monitor Work flow efficiency based on data patterns
• Cost containment, performance and outcomes management.

This data is then made available to the customer, latent or near real-time, for further statistical and regression analysis, business intelligence and data mining. This consortium partnership creates a knowledgeable community to monitor and grow the rules for population management, key performance indicators (KPIs), meaningful measures and recommended actions. Keeping In mind the healthcare data is never shared across consortium, hence keeping the unique business value of each consortium intact within their firewalls.          

In summary, WynTec’s Healthcare Data Framework permits the participation of a diverse range of small regional hospitals to very large multi-facility hospital and clinics to participate in the enterprise programs. Furthermore, the analytics services will leverage real-time data feeds, with insightful information presented via multi-device tablets.

WynTec’s vision is to deliver mobile analytics with near real-time feeds, interacting with latent enterprise data. Information can now be made available when the care giver desires it, how it should be delivered and where it should be presented.

Rock in the road photo courtesy of Washington State Dept. of Public Transportation, 2010