IBM is selling their AI for healthcare, known as Watson Health, to Francisco Partners for an estimated $1 billion. Watson Health is an artificial intelligence system created by IBM. The system is designed to work with healthcare providers and pharmaceutical companies to help improve the efficacy of treatments and reduce healthcare costs. Francisco Partners has extensive experience in healthcare technology including investments in Availity, eSolutions, Capsule, GoodRx, Landmark, QGenda, Trellis, and Zocdoc. The agreement will create a new standalone company that will continue serving existing provider, imaging, life sciences, payer and other healthcare clients, according to IBM and Francisco Partners.

Overpromise and Underdeliver

David Ferrucci pitched the idea of an AI engineered to identify word patterns and predict correct answers for the trivia game Jeopardy as a way to move natural language processing forward back in 2006. Watson uses IBM’s DeepQA software and the Apache UIMA (Unstructured Information Management Architecture) framework implementation. The room-sized supercomputer was able to leverage it’s 85,000 watts of power against Ken Jennings’ mere 20 watts. Watson won with a score of $77,147.

IBM’s executive leadership and marketing team were eager to capitalize on this heavily publicized event. IBM’s Ginni Rometty described described Watson Health as “moonshot” to revolutionize medicine with artificial intelligence and decided use Watson to help fight cancer. Ferrucci told executives Watson was designed specifically to meet the question and answer challenge format. Martin Kohn, chief medical scientist at IBM Research, wanted to use Watson more narrowly for tasks like predicting if an individual will have an adverse reaction to a drug. Neither were listened to and both soon moved on.

One of the main problems with Watson Health is that it is not always accurate. In one instance, Watson Health provided inaccurate information about a patient’s medical history, which could have had serious consequences. The Memorial Sloan Kettering Cancer Center found multiple examples of unsafe and incorrect treatment recommendations leading to the abandonment of Watson for Oncology. Watson could not read patient data when MD Anderson switched electronic health record systems leading to the collapse of Oncology Expert Advisor. The University of North Carolina School of medicine struggled with gaps and errors in the genetic data until Watson for Genomics was shelved. IBM spent billions buying businesses to improve Watson like Merge Healthcare, Phytel, Explorsys, and Truven Health Analytics but the quality issues persisted.

What’s next?

While Watson as a product was not a financial success for IBM, the underlying technology is strong. Just because it can’t cure cancer doesn’t mean it can’t do anything. IBM is focusing now on IBM Cloud. Users of IBM CloudPak will see AI being leveraged across the Data-as-a-Service platform.

Francisco Partners has been working with IBM and Watson in healthcare data and analytics for about five years. The current management team will continue to serve in similar roles in the new standalone company. Existing clients will still have have their accounts managed and supported. The deal included both the data and the analytics engine, so it remains to be seen whether its more profitable to keep Watson Health as is or break it up into parts.

If you want to understand more fully how this may impact your healthcare business, contact Juliet.Silver@perficient.com with Healthcare or Bill.Busch@perficient.com with Data Solutions.

I’m often asked how data and analytics can help to solve key industry problems in healthcare. With that in mind, three key industry issues rise to the top of the list.

1. Cost of Care Delivery

The cost of care delivery is at the center of the problems facing the healthcare Industry. Healthcare spending accounts for ~18% of US GDP. Although industry actors are working to increase the efficiency of care delivery, there is significant pressure on revenue with newer payment/reimbursement models making it difficult to even maintain historical financial parity.

There is a critical need to use data and analytics to identify trends that enable healthcare organizations to increase the effectiveness of care, reduce errors, better understand risk, reduce costs, increase operational efficiency, and capture maximum reimbursements for care delivery. Healthcare has been slow to implement modern data and analytics capabilities, leaving healthcare leaders without the proper information to make decisions and affect positive change.

2. Industry Consolidation

In the quest to increase efficiencies, industry consolidation has been rampant. Although consolidation promises long term operational efficiencies, it typically has a long payoff from an information visibility and insight perspective. Hospitals and payers are complex businesses and organizations, have complex data and applications systems, and are subject to many regulatory rules and hurdles, particularly around data security. When large players are combined, it typically takes years to achieve a reasonable level of consistency and access to data (information), which increases the blind spots mentioned above.

Healthcare organizations need help establishing a common view of data (information) across these complex organizations. If approached in the right way, modern data and analytics architectures, technologies and practices, collectively “Data and Analytics Programs,” can be leveraged to enable significant increases in efficiency and scale of data management and analytics systems, enabling a consistent and trusted view of healthcare data (information). And the relative cost of these modern data and analytics programs is typically well below that of the legacy programs and approaches.

3. Increase in Available Data

The proliferation of electronic health records systems, medical devices, and digital health has resulted in huge increases in the volume and variety of healthcare data, and is still picking up speed – this is truly Big Data. This presents vast opportunities to improve care through clinical research, improved care paths, mobile health and otherwise, however, it also presents significant data management and governance challenges for healthcare organizations.

Healthcare organizations are starved for the architectures, tools, processes, and policies needed to drive consistency, access, security, understanding, trust, and management of this deluge of Big Data. Unlocking the treasure trove of value held within this data requires implementing modern data management, analytics and governance systems, and programs to turn this data into information. This includes modern BI, predictive analytics, and artificial intelligence systems to enable forward-looking insight and action from this information.

What is Data Modernization?

There is a critical need in most healthcare organizations to modernize their data and analytics programs and capabilities to take advantage of the ever-growing amount of information available.

Data Modernization – Capabilities and Benefits

Common Use Cases

Leveraging data and analytics can be key in helping to make improvements, gain insights and realize efficiencies for multiple healthcare categories and needs, including:

• Reduced ED/Urgent Care Wait Times
• Readmission/Re-hospitalization Prediction and Reporting
• Contract Management (payer scorecards; contract performances, etc.)
• Patient Satisfaction
• Regulatory Items (HEDIS; Stars; P4P; ACO, etc.)
• Population Health (risk management; quality care; registry items)
• Leakage Analysis
• Utilization (though you could probably fold this into provider performance or service line)
• Labor Productivity (nursing hours; RVUs, etc.)
• Treatment and Medication Trend Analysis (top conditions, eligibility, risk score, cost-sharing, PMPY trend, Price and Use, High-Cost Claimants)
• Provider Performance Analysis – Efficiency ($s) and Quality
• Disease Management Analysis
• Facility Analysis
• Claims Denial Analysis
• Preventive Services (gaps in care)
• Disease Surveillance
• Diagnosis Prevalence
• Service Line Analytics

Summary

To achieve Healthcare’s Triple Aim (improving population health, improving patient experience, and reducing the cost of care), healthcare organizations need to take advantage of the insights available within the ever-increasing volume, variety, and velocity of data being produced in our always-on and always-connected world. Turning this data into insight requires leveraging modern data and analytics architectures and capabilities.

Thrilling our clients with innovation and impact – it’s not just rhetoric. This belief is instrumental for our clients’ success. In 2018, we introduced our Chief Strategists, who provide vision and leadership to help our clients remain competitive. Get to know each of our strategists as they share unique insights on their areas of expertise.

The current digital age has generated an exponential amount of data. From mobile devices and online activity to enterprise performance metrics and operational considerations, data is all around us. By 2025 worldwide data volumes are projected to grow to 175ZB, creating more opportunities for business leaders to develop informed strategies and make decisions based on their data.

We recently spoke with Arvind Murali, Data Governance Chief Strategist, to get his perspective on data governance, building data strategies to optimize business outcomes, and his life beyond the world of strategy.

What does your role as a Chief Strategist entail?

Arvind Murali: The AI and Digital technologies have launched the Fourth Industrial Revolution focused on machines supporting man and increasing efficiency and effectiveness. Companies across every industry are feeling the impact of digital and data transformation, and leaders are rethinking how technology and data can reshape their businesses.

As a Chief Strategist, I support my clients on thinking of Data as an Asset to use data in ways that they aren’t thinking about. I’m constantly listening to and learning from our clients about their business outcomes and opportunities. Then, I translate that feedback to help them build data strategy and governance to support and manage their data infrastructure.

By focusing on the business outcomes, we can build and implement data solutions that are broad enough to meet clients’ current needs and nimble enough to scale for future iterations.

What do you hope to accomplish as a Chief Strategist?

AM: Among my aspirations, I will continue supporting organizations with their digital and data transformation journeys. This support includes moving clients towards Data-as-a-Service (DaaS) models that directly creates bottom line opportunities. Some clients begin with a clean slate and want to capitalize on their legacy data assets. However, in that process, we determined that by marrying legacy data and enriching it with competitive and benchmarking data, it truly gives clients an edge. In the end, we’ve developed modern data platforms for them, allowing them to analyze their businesses, identify patterns, and adjust (as necessary) to compete in a digital market.

Above all, I always want to find purpose in my work by creating data solutions that make a difference for our clients and the customers and communities they serve. For instance, if our data-driven work provides a hospital with the ability to reduce a patient’s rate of readmission, that’s a meaningful end result. Or, if our manufacturing clients can simulate their components digitally and use analytics to enhance productivity, that increases their efficiency.

Strategically Speaking

Why does data governance matter for today’s enterprises?

AM: How often have you heard advertisements from Exxon, Best Buy, and Amazon stating that they use data and analytics for competitive advantage? Can you think of organizations making these statements five years ago? Technology has truly enabled data to become an asset that’s vital for any organization’s growth. It allows businesses to manage their supply chain, understand buying behavior, create personalized marketing, impact people’s lives, or streamline operations. If properly managed, businesses can use it to create a tangible return on their investments.

Data governance ultimately allows you to monitor, understand, measure, and own your data assets. This will lead to organizations creating competitive advantage based on their data assets.

How does implementing data governance impact businesses?

AM: Data governance [involves managing] data, culture, process, and technology. On one hand, companies rely on technology, such as AI and MDM, automation, and mastering customer data. However, the fundamental process of data ownership requires cultural acceptance [from the organization].

For example, some organizations have relied on employees to compile dense spreadsheet databases that contain massive amounts of data. The process of creating them is time consuming and mundane, but it’s a familiar process that executives have come to expect of their reports. When working with clients on data governance, we design and build a centralized, modern data platform to house and self-service their data. Once established, it’s a shift for employees because they’re now tasked to focus more on data analytics rather than building the database. Overall, the change will improve our clients’ productivity, but it’s upending long-held employee expectations.

By incorporating organizational change management with data governance, we can prepare workforces for the future and improve effectiveness and efficiency. If we’re working with industry-specific data assets like healthcare or financial services, we can also integrate our thought leadership in those areas to influence the process.

“Intelligent automation is already present in our daily lives, so it’s changing individuals’ perception about the technology. However, unifying an enterprise and shifting the business perception about intelligent automation [for data] is imperative for success.”

Why do businesses need a data strategy?

AM: By next year, businesses strategically using data will realize $430 billion in productivity benefits compared to competitors that aren’t using data. This translates into untapped potential of available data that can advance business growth, which is why developing a data strategy can mobilize those assets. Companies such as Facebook, Google, Salesforce, and Exxon have already implemented a strategy to convert data to information to insights, which has effectively differentiated their firms as dominant players in the digital space.

Setting a strategy for how you use data is essential because the technologies involved are constantly evolving. For most industries, impactful solutions will incorporate some form of automation, such as machine learning, AI, bots, or some other innovation. Being adaptable to the shifting landscape will only improve the final solution and future-proof your organization.

Think Like a Chief Strategist

Tell us about a recent project you’ve tackled. How did we help the client achieve success?

AM: We recently began work with a large hospital to build an end-to-end Data and AI platform. This work supports the client’s objective to become more patient-centric. Ultimately, we hope to improve patient outcomes, physician interaction, and overall efficiency.

A digital transformation journey for any organization takes time, and this situation is no different. A few months ago, the client had nothing established as far as a modern data platform or supporting processes. In fact, multiple departments established their own analytics and attempted to make decisions using data silos. Now, a centralized data platform allows for self-service, collaboration, and cross-departmental insights into knowledge that wasn’t previously possible. Although the project isn’t yet finished, the client is already realizing some significant benefits.

What questions do you ask a client when developing a data strategy?

AM: The top five questions every client needs to ask of their enterprise:

1. What data do we have?
2. What data do we need to have?
3. How do we use our data today?
4. How do we want to use it in the future?
5. How do we want to access our data?

These questions define our approach to creating data governance solutions that meet clients’ specific goals. Beyond that, these questions guide any enterprise that seek any form of digital transformation – they must embrace the startup mentality from the beginning.

How can businesses take a strategic approach to their data?

AM: A data strategy enables organizations to make informed decisions based on their data insights. Every data strategy focuses on three areas to optimize business outcomes:

1. Identifying which data sets are available for analysis and – more importantly – which are not
2. Building a modern data platform to host existing and targeted data
3. Developing data governance to make intelligent decisions based on data that’s been collected

This process should not revolve around departmental silos within an organization. Instead, developing a data strategy should start at the executive level and involve stewards from different business units. A strategy with visibility across the organization can help prioritize goals by identifying shared pain points, strategic objectives, and situations where overlaps exist.

“Always have a data strategy aligned to your business outcome. Data without outcomes is like a business without goals. It can be exciting to grow quickly at first, but it’s not a sustainable approach.”

Beyond the World of Strategy

What are your interests or hobbies when you’re not wearing the Chief Strategist hat?

AM: My two sons, a nine-year-old and a three-year-old, keep me busy outside of work between their activities and spending quality time with them. I often joke that I’ve played cricket since I was born. It’s something that’s in my blood. My sons have also grown to love cricket, so we enjoy playing together. I also really enjoy boxing, which is my favorite outlet for fitness.

Additionally, I’m an avid vlogger and discuss topics pertaining to technology, data, and being a “Smarchitect,” a term I’m hoping to trademark.

A Smarchitect is a smart-architect who doesn’t limit him/herself to one specialty and chooses to wear multiple architect hats. Being able to switch from one discipline to another at any point during a solution process, Smarchitects can define an end-to-end solution that prioritizes the business outcome by being agnostic on technology or capabilities needed to implement it.

Learn more about each of our Chief Strategists by following this series. 

My last blog in this series outlined virtual consults and the major role they play today in the patient experience. Today, I focus on the opportunities with better analytics and patient experience.

The emerging Salesforce/Cerner relationship is one of many indicators of a new paradigm in the culture of—as much as of the mechanics of—interoperability. Major players are recognizing that there is more long-term value in substantive collaboration than in simple open APIs. The industry is finally moving past the perception that owning data (or at least preferential access to data) has a value in its own right, and recognizing that value is only achievable in multi-party relationships that enable data siloes to actually merge and create new insight.

As this plays out across healthcare, it has a number of specific impacts.

First, the importance and demand for API and microservices capabilities, and for healthcare-specific capabilities in FHIR, will continue to expand rapidly. (Demand will outpace supply in this regard for some time, but healthcare technology has a habit of right-sizing quickly.)

To achieve the goals of personalization, digital care delivery and healthcare informatics generally, data management and a culture of collaborative integration is no longer a nice-to-have—it is essential. Organizations that recognize this dynamic and promote a culture of collaboration internally, as well as in their interactions with other entities, will be well positioned.

Secondly, and more speculatively, this is obviously one of the areas where blockchain use cases may ultimately be proven out in healthcare, but it is by no means the panacea some thought it would be—yet. This is precisely because it still relies on the same issue: a culture of actual collaboration between distinct entities for a common, commonly defined, goal—with no opt-outs—to be effective. That is easier said than done, especially in the fragmented, competitive-to-a-fault, healthcare world from which we are trying to evolve.

Yes, the outcome of this trend will be felt in substantial improvements in the quality of analytics and resultant insight for health systems, but more importantly, it will be felt in the personalized patient/consumer experience that finally begins to match other industries. Much has been made of the “creepiness” factor in personalized healthcare interactions (for example, a family

inadvertently learning of a sensitive issue affecting an adolescent child by virtue of personalized content). The only way to address these issues will be through the deployment of more sophisticated personalization protocols which are, of course, dependent upon better data.

Questions to Consider

• Is your organization optimized from a technology perspective for collaborative data sharing between environments to drive net new insight?
• Is your organization optimized from a culture perspective for collaborative data sharing between environments to drive net new insight?
• How effectively does your organization partner with third parties from both a technology and culture perspective? Are you limited by your appetite for risk or by a lack of viable use cases for collaboration?

To learn more about the top five digital health trends for providers, you can click here or download the guide below.

Discover how Northwell Health used participation in New York State’s Medicaid reform program to drive the development of a self-service analytics infrastructure. This session is included with your HIMSS 2019 registration.

Speakers Jim Kouba, Health Solutions Director, Perficient and Chris Hutchins, Associate Vice President, Healthcare Analytics, Northwell Health will dive into:

• Key techniques to build partnerships and establish a strong team approach between business and information and technology across the organization
• Best practices for providers wanting to build self-service analytics programs
• The importance of establishing a clinical enterprise data warehouse to form a single view of truth that enables Medicaid payment reform program participation

Our session will be held Wednesday, February 13th at 10:00am in room W308A.

Don’t miss this informative session at HIMSS 2019. We look forward to seeing you there!

Perficient is excited to showcase a comprehensive analytics and data strategy solution developed in partnership with Northwell Health at the 2019 HIMSS Global Conference and Exhibition.

In this session, Perficient and Northwell Health will discuss how the importance of aligning with business decision makers is integral to a successful analytics strategy and how incrementally building an enterprise data warehouse, and analytics system strengthens that collaboration.

“To meet the demands of its rapidly growing hospital, outpatient ambulatory and physician practices, Northwell Health needed an enterprise solution to bring together disparate data sources to deliver a holistic patient view,” said Jim Kouba, director of healthcare solutions at Perficient. “Through our partnership with Northwell, we helped develop a solution that enabled them to deliver better care throughout the patient’s journey.”

During HIMSS19, Perficient and Northwell Health will present the tools, processes, education, and measures for enabling information and discovery analysis across clinical, financial and operational functions.

The session, “Overcoming Challenges in Creating Self-Service Analytics,” will take place on Wednesday, February 13, from 10 – 11 a.m. EST and is provided as part of the Data Science, Analytics, Clinical, and Business Intelligence track.

“We’re honored to have been invited to speak and present with Northwell Health at HIMSS19,” said Matt Castle, vice president of Industry Services at Perficient. “We are thrilled for the opportunity to share best practices and techniques with leading healthcare providers.”

To learn more about Perficient and Northwell Health’s presentation, please read the full press release here.

Historically, a key driver for the establishment and application of governance has been to address regulatory and compliance concerns. The business intelligence and analytics space has been an early adopter of the practice, and the evolution of governance adoption is starting to expand into the consumer, patient and member engagement and experience domains.

These are no less important today, but clearly demonstrate the “reach” of governance is broadening across a much wider band of the enterprise’s business.

Healthcare, like other industries, is seeing governance move into the operational world, and process owners are adopting the practice more frequently. This is likely due to the ongoing quest for containing cost and optimizing processes where healthcare organizations see governance as being able to deliver on those fronts.

Governance is becoming paramount as the use of information, content, and knowledge continues to expand across a wider variety of business needs including supporting internal processes, developing new services, providing a better patient and member experience, driving towards operational excellence, and adapting new business models.

Implementing a governance program to support these use cases requires a focused involvement from leadership and subject matter experts (SME) in addition to the technical or regulatory staff historically associated with data governance. It will be the business stakeholders and SMEs who will be able to articulate what is needed to support the use cases.

For example, where data gathering and quality errors exist, or where conflicting content and knowledge exists, these individuals can recommend how best to address these errors to ensure the information, content, and knowledge is fit for purpose and drives the desired outcomes.

All of this is indicative of the need for governance to focus upon – and contribute to the success of – business outcomes. For governance to effectively do this it needs to continue on the path of being installed as a holistic enterprise-wide business capability ensuring a business (versus IT-only) perspective.

This blog was co-authored by Mark Steinbacher and Priyal Patel.

To learn more about the rebirth of governance in healthcare, and exploring the trends and impact on patients and organizational operations, you can download the guide below.

So far in this blog series I have discussed using the Cynefin framework for providing guidance in determining the best SDLC methodology to use for a particular type of project defined by the framework as well as delving into the Chaotic type of project.

This month I will focus on the Complicated type of projects defined using the Cynefin framework.

As you can see from the picture above, Complicated is the category where good practices can be found. In this category there are multiple right answers, and expert diagnosis is required to figure them out. This sector demands more quantitative approaches such as Six Sigma as an example.

There are several key characteristics which assist in identifying a complicated project.

Characteristics of the Complicated category:

• Multiple right answers are available
• A general idea of the known unknowns
• You know the questions you need to answer
• Don’t know how to obtain the answers
• The problem is more predictable than unpredictable
• Cause and Effect relationship is not immediately known but is discoverable given enough time

If you find yourself managing a complicated project the approaches defined below will help you better define the complex problems.

Approach for Complicated problems:

• Assess the situation and Sense the problem
• Investigate several options
• Analyze large data groups, as needed
• Use experts knowledge to gain insight
• Use metrics to gain control
• Base response on good practice
• Determine a course of action
• Execute the plan, following the Plan, Do, Check, Act cycle

As the picture above indicates, since this is a more quantitative type of project the waterfall methodology can be used with tenants of Agile.

So far in this blog series I have discussed using the Cynefin framework for providing guidance in determining the best SDLC methodology to use for a particular type of project defined by the framework as well as delving into the chaotic type of project.

This month I will focus on the Complex type of projects defined using the Cynefin framework.

As you can see from looking at the picture above, the complex is the category where solutions are discovered by developing a safe environment for experimentation. This experimentation allows you to discover important information that leads to the creation of new emergent solutions.

These problems are always more unpredictable than they are predictable. Hindsight can only tell us if there is a right answer as we explore the problem. Only with detailed experiments, inspections and results will you be able to base your decisions.

The current results are then used to define the next step toward a solution. In such situations, the ability to probe (explore), sense (inspect) and respond (adapt) is critical. There are several key characteristics which assist in identifying a complex project.

Characteristics of the Complex category:

• There are unknown unknowns
• Even the starting point requires experimentation
• The right questions to ask need exploration
• The solution is only apparent once discovered
• The sector of emergence ideas
• Routine solutions don’t apply
• Higher levels of interaction and communication are essential

If you find yourself managing a complex project the approaches defined below will help you better define the complex problems.

Approach for Complex problems:

• Explore to learn about the problem, as they require more creativity and innovative thinking skills
• Develop a theory and experiment to gather more knowledge
• Experimentation to discover patterns and gain more knowledge
• Repeat as necessary, with the goal of moving your problem into the another category
• Execute and evaluate, following the Plan, Do, Check, Act cycle

As the picture above indicates, Agile is the desired methodology to use as theory. Experimentation can be defined in predetermined amounts of time with predefined goals.

Monitoring website traffic and evaluating its performance are the first steps in web analytics for your provider site. Analyzing web data to identify ways to better engage patients-consumers across the site is fundamental. Optimizing key visitor task conversion funnels and working towards improving the overall health of your site are just a few ideas to keep in mind.

Here are some questions you should be asking on a regular basis about analytics:

What does a spike in traffic truly mean? Is this qualified traffic?

When spikes in traffic are discovered, it’s important to understand where your site traffic is coming from and what this means for your site. Consider looking at a breakdown of website traffic by key metrics such as page views, session time, visitors, conversion events (schedule an appointment, find a doctor, find a location etc.) and conversion rates. When you understand where spikes are coming from and how qualified the traffic is, this will assist you in identifying whether the additional visits are likely to turn into new patients.

How are patients and potential consumers engaging with your site?

In addition to monitoring performance of key lead generation/conversion tasks, it’s worth taking a deeper look at engagement. These insights will help you identify how many pages are visited per session, time on site/time on page, repeat visitors, and exchanging of information to name a few. This will help paint a picture of what your website visitors are doing and support you in understanding your audience when making changes to the site and assisting with optimization efforts.

Do you evaluate performance by clinical area for the services provided?

Exploring traffic by different segments such as by clinical area (Heart, Cancer, Orthopedics etc.) across your provider site will provide a comprehensive view into specific patient-consumer digital interactions. Understanding conversion and direction such as drop off points within the funnel (for schedule an appointment, setting up a patient portal account, find a doctor, send a message to name a few), will support you in identifying areas for improvement for specific pages on the site.

Using web analytics data that also includes web session playbacks, online survey data, and heat maps helps to provide a comprehensive view of what is happening across your provider site. With deeper understanding of patient-consumer behavior, you’ll be able to drive recommendations, test hypothesis and optimize conversion funnels across your provider site to ensure you meet patient-consumer needs across your site. By no means is this an easy task.

I would love to hear how you are using web analytics to improve patient-consumer interactions. Please comment below.

In today’s world implementing effective EDI strategy is very challenging. Every enterprise needs to have visibility, whether or not EDI transactions are in compliance with established the policies and procedures to effectively manage their businesses.

With a high emphasis on compliance and accountability, every important step of the transaction lifecycle needs to be tracked, inspected, monitored and exposed to Business/IT users within the organization and external partners when required.

Handling EDI transactions means parsing, validating, splitting, processing, creating and responding at numerous stages before they reach to downstream system. The more steps the transaction goes through, the more risk for leadership (Business/IT) to lose visibility and ability to reconcile transactions.

Challenges You’ll Face Without An EDI Strategy:

1. Unable to reconcile EDI transactions to meet the required levels of service.
2. Involving technical department every time a submitter inquires about a transaction.
3. Lack of visibility into production EDI operations.
4. Lack of reporting around submitter, transaction and origination level.

In order to effectively implement the EDI strategy, the Edifecs Transaction Manager should be a leading solution for transaction lifecycle management. This helps in minimizing service costs pertaining to compliance, security, technology etc. by providing a comprehensive view of the transaction lifecycle to internal users and partners.

Transaction Manager Helps:

• Achieve Accountability Across The Entire Lifecycle:
• Leverage Transaction Lifecycle Insight to Make Better-Informed Decisions
• Improve First Pass Rates
• Help Your Submitters Troubleshoot Their Own Problems
• Reduce Operating Costs Through Management by Exception
• Eliminate IT Bottlenecks by Empowering Business Users
• Reduce Transaction Errors and Rework
• Transaction Monitoring
• Transaction Reconciliation and Response Documents
• Notifications and Alerts
• Transaction Search and Business Views
• Reporting
• Event Tracking
• Unified Trading Partner Management
• Healthcare Transaction Repository
• File Submission and Retrieval over the Web
• Transaction Validation and Acknowledgements
• Scalability and Performance

Edifecs Transaction Management works by integrating with middleware infrastructure, the messaging engine or enterprise service bus to automatically review the transactions at each point in the process; to record key events in the solution.

The captured information is analyzed and the appropriate associations between related transactions are created so that a complete picture of the entire process is obtained.

Finally this information is presented through a rich web interface that provides business/Data/Smart views and drilldowns into the data. Which allows for customized dashboards, alerts/notifications and reporting.

Imagine a spreadsheet has been replicated thousands of times across a vast network of computers. Now, imagine that these computers, within the network, regularly update this spreadsheet. This is the most basic form of understanding a blockchain.

Blockchain is the underlying technology in bitcoin and other cryptocurrencies. Information contained and stored within a blockchain will exist as a shared, and continually reconciled, database. These databases aren’t stored on a single computer and therefore are easily accessible to the public and are even more easily verifiable.

The two most commonly cited examples of how blockchain can be an assistance in healthcare are data interoperability and unlimited security. The stream of new possibilities is endless. This efficient system of security and efficiency could be referred to as the Holy Grail of a consistent and precise health record by securing data, as it’s being exchanged among organizations for various clinicians across the spectrum.

Blockchain is the single immutable source of truth for data and transactions that span multiple parties.

“Blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record, not just financial transactions but virtually everything of value.”

Don & Alex Tapscott, authors of Blockchain Revolution.

What Makes Up a Blockchain?

• Shared Ledger
  • Ledger is shared and distributed across business network as an append only system of records.
• Privacy
  • Every transaction is authenticated, verified and provided only the appropriate level of visibility
• Smart Contract
  • Ability to embed IF/THEN conditions as a part of transaction (block)
• Consensus
  • Each participating network node must provide agreement before each transaction (block) is considered as a part of ledger

Blockchain Solves What Problems?

Blockchain lets a business agree on transaction history. The history is managed and able to be tracked to any specific point or individual to ensure accountability. This process allows for all parties to be able to agree on the state of the system.

Cryptography is used to ensure that the network participants only see the part of the ledger that is relevant to them and that the transactions are secure, authenticated, and verifiable in the context of permissioned business blockchains.

Blockchain Resiliency

Blockchain has a built-in sturdiness, by storing blocks of data that are identical across the network of computers. This network of data means that the blockchain cannot:

• Be controlled by a single entity
• Have a single point of failure

The Enhanced Aspect of Security

Containing data across a vast network of computers and databases with a blockchain, virtually eliminates the possible risk that can come from data being stored in a single location. With the help of encryption technology security methods, a blockchain surpasses the username/password format of security.

By the secure and dependable nature, a blockchain is well maintained and positioned to be a functional aspect of a solution to a variety of problems in healthcare. A successful blockchain instituted into a business model can help promote efficiency by; cutting costs from eliminating third-party legal entities, updating security and cryptography methods, and can establish a trustworthy system of checks and balances.

Learn more about blockchain by signing up for Perficient’s informational webinar, Blockchain: Beyond the Hype, Tuesday, April 3.