Tag Archives: Healthcare

Best Kept Secrets for Successful Data Governance

data governance

Best Kept Secrets for a Successful Data Governance

If you’ve spent some time studying and practicing data governance, you would agree that data governance is a challenging yet rewarding endeavor.  Across industries, a growing number of organizations have put data governance programs in place so they can more effectively manage their data to drive the business value. But the reality is, data governance is a complex process, and most companies practicing data governance today are still at the early phase of this very long journey.  In fact, according to the result from over 240 completed data governance assessments on http://governyourdata.com/, a community website dedicated to everything data governance, the average score for data governance maturity is only 1.6 out of 5. It’s no surprise that data governance was a hot topic at last week’s Informatica World 2015.  Over a dozen presentations and panel discussions on data governance were delivered; practitioners across various industries shared their real-world stories on topics ranging from how to kick-start a data governance program, how to build business cases for data governance, frameworks and stewardship management, to the choice of technologies.  For me, the key takeaways are:

  1. Old but still true – To do data governance the right way, you must start small and focus on achieving tangible results. Leverage the small victories to advance to the next phase.
  1. Be prepared to fail more than once while building a data governance program. But don’t quit, because your data will not.
  1. Why doesn't it fit?!One-size doesn’t fit all when it comes to building a data governance framework, which is a challenge for organizations, as there is no magic formula that companies can immediately adopt. Should you build a centralized or federated data governance operation? Well, that really depends on what works within your existing environment.
    In fact, when asked “what’s the most challenging area for your data governance effort” in our recent survey conducted at Informatica World 2015, “Identify roles and responsibilities” got the most mentions. Basic principle? – Choose a framework that blends well with your company‘s culture.
  1. pptLet’s face it, data governance is not an IT project, nor is it about fixing data problems. It is a business function that calls for people, process and technology working together to obtain the most value from your data. Our seasoned practitioners recommend a systematic approach: Your first priority should be people gathering – identifying the right people with the right skills and most importantly, those who have a passion for data; next is figuring out the process. Things to consider include: What’s the requirement for data quality? What metrics and measurements should be used for examining the data; how to handle exceptions and remediate data issues? How to quickly identify and apply security measures to the various data sets?  Third priority is selecting the right technologies  to implement and facilitate those processes to transform the data so it can be used to help meet  business goals.
  1. Business & IT Collaboration“Engage your business early on” is another important tip from our customers who have achieved early success with their data governance program. A data governance program will not be sustainable without participation from the business. The reason is simple – the business owns the data, they are the consumers of the data and have specific requirements for the data they want to use. IT needs to work collaboratively with business to meet those requirements so the data is fit for use, and provides good value for the business.
  1. Scalability, flexibility and interoperability should be the key considerations when it comes to selecting data governance technologies. Your technology platform should be able to easily adapt to the new requirements arising from the changes in your data environment.  A Big Data project, for example, introduces new data types, increased data speed and volume. Your data management solution should be agile enough to address those new challenges with minimum disruption to your workflow.

Data governance is HOT! The well-attended sessions at Informatica World, as well as some of our previously hosted webinars is testimony of the enthusiasm among our customers, partners, and our own employees on this topic. It’s an exciting time for us at Informatica because we are in a great position to help companies build an effective data governance program. In fact, many of our customers have been relying on our industry-leading data management tools to support their data governance program, and have achieved results in many business areas such as meeting compliance requirements, improving customer centricity and enabling advanced analytics projects. To continue the dialogue and facilitate further learning, I’d like to invite you to an upcoming webinar on May 28, to hear some insightful, pragmatic tips and tricks for building a holistic data governance program from industry expert David Loshin, Principal at Knowledge Integrity, Inc,  and Informatica’s own data governance guru Rob Karel.

Get Ready!

Get Ready!

“Better data is everyone’s job” –  well said by Terri Mikol, director of Data Governance at University of Pittsburgh Medical Center.  For companies striving to leverage data to deliver business value, everyone within the company should treat data as a strategic asset and take on responsibilities for delivering clean, connected and safe data. Only then can your organization be considered truly “Data Ready”.

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Data Governance Maturity at Your Organization?

Data Governance

Data Governance Maturity

If you are a Healthcare provieder, having a complete view of your member’s data will improve your service. The more detail you have on each member, the more effective you can be at providing the service they deserve.

It is probable that all of the information on a member is stored in several different systems – so getting the complete picture can be difficult. In addition – controlling access to this information is an important part of any organization’s overall strategy. And finally – data assets become more valuable the more you use them. If three divisions of an organization all share information about their interactions with a customer, the organization as a whole is better able to service the customer, at lower cost and with high customer satisfaction.

Data governance is used by organizations to exercise control over processes and methods used by their data stewards and data custodians in order to improve data quality. Data governance is a control that ensures that the data entry by an operations team member or by an automated process meets precise standards, such as a business rule, a data definition and data integrity constraints in the data model. A data governor uses data quality monitoring against production data to communicate errors in data back to operational team members, or to the technical support team, for corrective action.

How far along in the Data Governance journey is your organization?

  1. Is your organization currently unaware of Data Governance?

There is minimal focus on data quality or security, data isn’t prioritized in any meaningful or actionable way, there is no measurement around data governance and it isn’t managed.

  1. Is your organization in the initial phases of Data Governance?

Data Governance is primarily grassroots driven by a few passionate individuals, rules are implemented in an ad hoc fashion, with policies or standards are part of functional requirements in an IT project, which is only considered successful if the IT release is considered successful.

  1. Is Data Governance at your organization repeatable?

For these organizations – data governance is still grassroots, but gaining attention at the IT management level. There are documented IT governance and standards driving metadata resuse and improved collaboration across IT projects. The success is measured primarily on improved IT efficiencies. This is typically managed through a pilot project.

  1. Defined Data Governance

This is lead primarily from senior IT through adoption of competency centers and centers of excellence. Project leadership is primarily through IT, but there is business involvement. The success is measured on operational metrics at a project level.

  1. Data Governance that is Managed

The Data Governance program is sponsored by business leaders, initiated as part of a broader strategic enterprise information management program. Data Governance will live through multi-phase, multi-year efforts but measured based on the success of the program.

  1. Optimized Data Governance

There is top-level executive sponsorship and support. Data governance is embraced as a self-sustaining core business function managing data as a corporate asset. Success is measured on the total impact to the business, not just confined to specific programs or strategies.

 
There is a fantastic site (http://governyourdata.com/ ) that is an open peer-to-peer community of data governance practitioners, evangelists, thought leaders, bloggers, analysts and vendors. The goal of the governyourdata community is to share best practices, methodologies, frameworks, education, and other tools to help data governance leaders succeed in their efforts.

One of our customers, UPMC has a great blog post on their implementation of a Data Governance council and the challenges they faced making it a priority in their organization.

To figure out where on the continuum of data governance maturity – there is a Data Governance Maturity Assessment Tool through the governyourdata.com site. A maturity assessment level sets current gaps and strengths and paves the way for defining a successful strategy. The process of assessing an organization’s maturity should include interviews of relevant business and IT staff, business risk surveys, business analyst time and activity analysis, and other techniques. Once your assessment is completed – you can identify the appropriate steps you need to plan for to develop an Optimized Data Governance approach for your organization. Where does your organization stand?

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Healthcare at Informatica World 2015

Healthcare at Informatica World 2015

Informatica World 2015

If you follow me on LinkedIn than you already know that there is no place I would rather be than in front of a client – virtually or in person. There is simply nothing that energizes me more than gathering the insights from client advocates. With this said, it will be no surprise that Informatica World makes me giddy; like a kid in a candy store – over 1500 clients telling their stories and sharing valuable lessons learned.

For healthcare alone, over a dozen payer and provider organizations have volunteered to share their use cases, their stories and their lessons learned. The array of brands represented is second to none; i.e. Kaiser, UPMC, Cleveland Clinic and Humana.

Beyond sessions, clients ask for more opportunities to network with peers and get hands on with the next releases of products and we listen!

  • Healthcare cocktail reception Tuesday evening
  • Healthcare Industry breakfast Thursday morning
  • Hands on Labs with industry specific content
  • Partner technology showcase

A complete list of healthcare sessions + a few you hot topic sessions is below. I look forward to seeing you in Las Vegas next week!

Informatica World 2015 - Healthcare

Informatica World 2015 – Healthcare

 

 

 

 

 

 

 

 

 

 

 

 

 

Informatica World 2015 - Healthcare

Informatica World 2015 – Healthcare

 

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Healthcare Transformation Demands Data Centricity

healthcare

Healthcare Transformation Demands Data Centricity

The U.S. Department of Health and Human Services declares that approximately 75 percent of the country’s eligible professionals and more than 91 percent of hospitals are on electronic health records certified for Stage 1 meaningful use.  These applications along with many others are creating valuable electronic data that – when integrated, shared and analyzed – can propel transformational initiatives like accountable care, population health and risk based contracting.

Success should not be limited by technology given other industries have demonstrated that near real time sharing and analysis of sensitive data is quite possible. What, then, could hold healthcare back from success?

Informatica recently released the findings of a survey targeted at understanding the answer to this very question. Respondents revealed that while 85% of are effective at putting financial data to use to inform decision making, many less are confident about putting data to use to inform patient engagement initiatives requiring access to external data and big data which they note to be more challenging. Complex, cross organizational transformational business processes require information sharing between applications yet over 65% of respondents say data integration and data quality are significantly challenging. So healthcare organizations are collecting data but many have yet to integrate these silos of application data to realize its full potential.

Similarly, International Institute of Analytics, a little over a year ago, offered a view of the healthcare analytics maturity landscape based upon deep quantitative assessments of more than 20 healthcare provider organizations.  This research uncovered two important facts:

  1. Hospitals and other healthcare provider organizations have gone to great effort to implement core components of an EMR, giving them access to large amounts of data on patients, processes and costs.
  2. Those data assets have not yet been put to their highest and best use.

This is not uncommon, across industries and firms of all shapes and sizes, many have confronted the question of how well they are leveraging data and analytics to transform their organizations. Those organizations that have made the most progress in revealing meaningful and differentiated insights are those that have intentionally built and funded enterprise information management or data management programs in support of analytics. These programs accelerate stakeholder access to trusted information when and where they need it.

Informatica worked with International Institute of Analytics to publish a new whitepaper that explores this issue, with detailed definitions for how IIA measures analytics maturity within healthcare as an independent third-party. This report looks at how provider organizations are approaching their investments in analytics, with a focus on essential attributes like data quality and management, leadership support, the culture of data, analytics talent, and an enterprise-wide approach to analytics. If you haven’t read it yet, I urge you to read the report.

If you’d like to talk about these ideas, visit Informatica at HIMSS15 in Booth #3056.

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IDMP Field Notes: Compliance Trends in Q1 2015

With the European Medicines Agency (EMA) date for compliance to IDMP (Identification of Medicinal Products) looming, Q1 2015 has seen a significant increase in IDMP activity.  Both Informatica & HighPoint Solution’s IDMP Round Table in January, and a February Marcus Evans conference in Berlin provided excellent forums for sharing progress, thoughts and strategies.  Additional confidential conversations with pharmaceutical companies show an increase in the number of approved and active projects, although some are still seeking full funding.  The following paragraphs sum up the activity and trends that I have witnessed in the first three months of the year.

I’ll start with my favourite quote, which is from Dr. Jörg Stüben of Boehringer Ingelheim, who asked:

“Isn’t part of compliance being in control of your data?” 

I like it because to me it is just the right balance of stating the obvious, and questioning the way the majority of pharmaceutical companies approach compliance:  A report that has to be created and submitted.  If a company is in control of their data, regulatory compliance would be easier and come at a lower cost.  More importantly, the company itself would benefit from easy access to high quality data.

Dr. Stüben’s question was raised during his excellent presentation at the Marcus Evans conference.  Not only did he question the status quo, but proposed an alternate way for IDMP compliance:  Let Boehringer benefit from their investment in IDMP compliance.   His approach can be summarised as follows:

  • Embrace a holistic approach to being in control of data, i.e. adopt data governance practices.
  • This is not about just compliance. Include optional attributes that will deliver value to the organisation if correctly managed.
  • Get started by creating simple, clear work packages.

Although Dr Stüben did not outline his technical solution, it would include data quality tools and a product data hub.

At the same conference, Stefan Fischer Rivera & Stefan Brügger of Bayer and Guido Claes from Janssen Pharmaceuticals both came out strongly in favour of using a Master Data Management (MDM) approach to achieving compliance.  Both companies have MDM technology and processes within their organisations, and realise the value a MDM approach can bring to achieving compliance in terms of data management and governance.  Having Mr Claes express how well Informatica’s MDM and Data Quality solutions support his existing substance data management program, made his presentation even more enjoyable to me.

Whilst the exact approaches of Bayer and Janssen differed, there were some common themes:

  • Consider both the short term (compliance) and the long term (data governance) in the strategy
  • Centralised MDM is ideal, but a federated approach is practical for July 2016
  • High quality data should be available to a wide audience outside of IDMP compliance

The first and third bullet points map very closely to Dr. Stüben’s key points, and in fact show a clear trend in 2015:

IDMP Compliance is an opportunity to invest in your data management solutions and processes for the benefit of the entire organisation.

Although the EMA was not represented at the conference, Andrew Marr presented their approach to IDMP, and master data in general.  The EMA is undergoing a system re-organisation to focus on managing Substance, Product, Organisation and Reference data centrally, rather than within each regulation or program as it is today.  MDM will play a key role in managing this data, setting a high standard of data control and management for regulatory purposes.  It appears that the EMA is also using IDMP to introduce better data management practice.

Depending on the size of the company, and the skills & tools available, other non-MDM approaches have been presented or discussed during the first part of 2015.  These include using XML and SharePoint to manage product data.  However I share a primary concern with others in the industry with this approach:  How well can you manage and control change using these tools?  Some pharmaceutical companies have openly stated that data contributors often spend more time looking for data than doing their own jobs.  A XML/SharePoint approach will do little to ease this burden, but an MDM approach will.

Despite the others approaches and solutions being discovered, there is another clear trend in Q1 2015

MDM is becoming a favoured approach for IDMP compliance due to its strong governance, centralised attribute-level data management and ability to track changes.

Interestingly, the opportunity to invest in data management, and the rise of MDM as a favoured approach has been backed up with research by Gens Associates.  Messers Gens and Brolund found a rapid incGens Associates IA with captionrease in investment during 2014 of what they term Information Architecture, in which MDM plays a key role.  IDMP is seen as a major driver for this investment.  They go on to state that investment  in master data management programs will allow a much easier and cost effective approach to data exchange (internally and externally), resulting in substantial benefits.  Unfortunately they do not elaborate on these benefits, but I have placed a summary on benefits of using MDM for IDMP compliance here.

In terms of active projects, the common compliance activities I have seen in the first quarter of 2015 are as follows:

  • Most companies are in the discovery phase: identifying the effort for compliance
  • Some are starting to make technology choices, and have submitted RFPs/RFQs
    • Those furthest along in technology already have MDM programs or initiatives underway
  • Despite getting a start, some are still lacking enough funding for achieving compliance
    • Output from the discovery phase will in some cases be used to request full funding
  • A significant number of projects have a goal to implement better data management practice throughout the company. IDMP will be the as the first release.

A final trend I have noticed in 2015 is regarding the magnitude of the compliance task ahead:

Those who have made the most progress are those who are most concerned about achieving compliance on time. 

The implication is that the companies who are starting late do not yet realise the magnitude of the task ahead.  It is not yet too late to comply and achieve long term benefits through better data management, despite only 15 months before the initial EMA deadline.  Informatica has customers who have implemented MDM within 6 months.  15 months is achievable provided the project (or program) gets the focus and resources required.

IDMP compliance is a common challenge to all those in the pharmaceutical industry.  Learning from others will help avoid common mistakes and provide tips on important topics.  For example, how to secure funding and support from senior management is a common concern among those tasked with compliance.  In order to encourage learning and networking, Informatica and HighPoint Solutions will be hosting our third IDMP roundtable in London on May 13th.  Please do join us to share your experiences, and learn from the experiences of others.

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Analytics Stories: A case study from UPMC

UPMC

As I have shared within the posts of this series, businesses are using analytics to improve their internal and external facing business processes and to strengthen their “right to win” within the markets that they operate. Like healthcare institutions across the country, UPMC is striving to improve its quality of care and business profitability. One educational healthcare CEO put it to me this way–“if we can improve our quality of service, we can reduce costs while we increase our pricing power”. In UPMC’s case, they believe that the vast majority of their costs are in a fraction of their patients, but they want to prove this with real data and then use this information drive their go forward business strategies.

Getting more predictive to improved outcomes and reduce cost

AnalyticsArmed with this knowledge, UPMC’s leadership wanted to use advanced analytic and predictive modeling to improve clinical and financial decision making. And taking this action was seen as producing better patient outcomes and reducing costs. A focus area for analysis involved creating “longitudinal records” for the complete cost of providing particular types of care. For those that aren’t versed in time series analysis, longitudinal analysis uses a series of observations obtained from many respondents over time to derive a relevant business insight. When I was also involved in healthcare, I used this type of analysis to interrelate employee and patient engagement results versus healthcare outcomes. In UPMC’s case, they wanted to use this type of analysis to understand for example the total end to end cost of a spinal surgery. UPMC wanted to look beyond the cost of surgery and account for the pre-surgery care and recovery-related costs. However, to do this for the entire hospital meant that it needed to bring together data from hundreds of sources across UPMC and outside entities, including labs and pharmacies. However, by having this information, UPMC’s leadership saw the potential to create an accurate and comprehensive view which could be used to benchmark future procedures. Additionally, UPMC saw the potential to automate the creation of patient problem lists or examine clinical practice variations. But like the other case studies that we have reviewed, these steps required trustworthy and authoritative data to be accessed with agility and ease.

UPMC’s starts with a large, multiyear investment

In October 2012, UPMC made a $100 million investment to establish an enterprise analytics initiative to bring together for the first time, clinical, financial, administrative, genomic and other information together in one place. Tom Davenport, the author of Competing on Analytics, suggests in his writing that establishing an enterprise analytics capability represents a major step forward because it allows enterprises to answer the big questions, to better tie strategy and analytics, and to finally rationalize applications interconnect and business intelligence spending. As UPMC put its plan together, it realized that it needed to impact more than 1200 applications. As well it realized that it needed one system manage with data integration, master data management, and eventually complex event processing capabilities. At the same time, it created the people side of things by creating a governance team to manage data integrity improvements, ensuring that trusted data populates enterprise analytics and provides transparency into data integrity challenges. One of UPMC’s goals was to provide self-service capabilities. According to Terri Mikol, a project leader, “We can’t have people coming to IT for every information request. We’re never going to cure cancer that way.” Here is an example of the promise that occurred within the first eight months of this project. Researchers were able to integrate—for the first time ever– clinical and genomic information on 140 patients previously treated for breast cancer. Traditionally, these data have resided in separate information systems, making it difficult—if not impossible—to integrate and analyze dozens of variables. The researchers found intriguing molecular differences in the makeup of pre-menopausal vs. post-menopausal breast cancer, findings which will be further explored. For UPMC, this initial cancer insight is just the starting point of their efforts to mine massive amounts of data in the pursuit of smarter medicines.

Building the UPMC Enterprise Analytics Capability

To create their enterprise analytics platform, UPMC determined it was critical to establish “a single, unified platform for data integration, data governance, and master data management,” according to Terri Mikol. The solution required a number of key building blocks. The first was data integration to collect and cleanses data from hundreds of sources and organizes them into repositories that would enable fast, easy analysis and reporting by and for end users.

Specifically, the UPMC enterprise analytics capability pulls clinical and operational data from a broad range of sources, including systems for managing hospital admissions, emergency room operations, patient claims, health plans, electronic health records, as well as external databases that hold registries of genomic and epidemiological data needed for crafting personalized and translational medicine therapies. UPMC has integrated quality checked source data in accordance with industry-standard healthcare information models. This effort included putting together capabilities around data integration, data quality and master data management to manage transformations and enforce consistent definitions of patients, providers, facilities and medical terminology.

As said, the cleansed and harmonized data is organized into specialized genomics databases, multidimensional warehouses, and data marts. The approach makes use of traditional data warehousing approaches as well as big data capabilities to handle unstructured data and natural language processing. UPMC has also deployed analytical tools that allow end users to exploit the data enabled from the Enterprise Analytics platform. The tools drive everything from predictive analytics, cohort tracking, and business and compliance reporting. And UPMC did not stop here. If their data had value then it needed to be secured. UPMC created data audits and data governance practices. As well, they implemented a dynamic data masking solution ensures data security and privacy.

Parting Remarks

As I have discussed, many firms are pushing point silo solutions into their environments, but as UPMC shows this limits their ability to ask the bigger business questions or in UPMC’s case to discover things that can change people’s live. Analytics are more and more a business enabler if they are organized as an enterprise analytics capability. As well, I have come to believe that analytics have become foundational capability to all firms’ right to win. It informs a coherent set of capabilities and establishes a firm’s go forward right to win. For this, UPMC is a shining example of getting things right.

Related links

Detailed UPMC Case Study

Related Blogs

Analytics Stories: A Banking Case Study

Analytics Stories: A Financial Services Case Study

Analytics Stories: A Healthcare Case Study

Who Owns Enterprise Analytics and Data? Competing on Analytics: A Follow Up to

Thomas H. Davenport’s Post in HBR

Thomas Davenport Book “Competing On Analytics”

Author Twitter: @MylesSuer

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Healthcare’s Love Hate Relationship with Data

Healthcare's Love Hate Relationship with Data

Healthcare’s Love Hate Relationship with Data

Healthcare and data have the makings of an epic love affair, but like most relationships, it’s not all roses. Data is playing a powerful role in finding a cure for cancer, informing cost reduction, targeting preventative treatments and engaging healthcare consumers in their own care. The downside? Data is needy. It requires investments in connectedness, cleanliness and safety to maximize its potential.

  • Data is ubiquitous…connect it.

4400 times the amount of information held at the Library of Congress – that’s how much data Kaiser Permanente alone has generated from its electronic medical record. Kaiser successfully makes every piece of information about each patient available to clinicians, including patient health history, diagnosis by other providers, lab results and prescriptions. As a result, Kaiser has seen marked improvements in outcomes: 26% reduction in office visits per member and a 57% reduction in medication errors.

Ongoing value, however, requires continuous investment in data. Investments in data integration and data quality ensure that information from the EMR is integrated with other sources (think claims, social, billing, supply chain) so that clinicians and decision makers have access in the format they need. Without this, self-service intelligence can be inhibited by duplicate data, poor quality data or application silos.

  • Data is popular…ensure it is clean.

Healthcare leaders can finally rely on electronic data to make strategic decisions. A CHRISTUS Health anecdote you might relate to – In a weekly meeting each executive reviews their strategic dashboard; these dashboards drive strategic decision making about CPOE adoption (computerized physician order entry), emergency room wait times and price per procedure. Powered by enterprise information management, these dashboards paint a reliable and consistent view across the system’s 60 hospitals. Previous to the implementation of an enterprise data platform, each executive was reliant on their own set of data.

In the pre-data investment era, seemingly common data elements from different sources did not mean the same thing. For example, “Admit Date” in one report reflected the emergency department admission date whereas “Admit Date” in another report referred to the inpatient admission date.

  •  Sharing data is necessary…make it safe.

To cure cancer, reduce costs and engage patients, care providers need access to data and not just the data they generate; it has to be shared for coordination of care through transitions of care and across settings, i.e. home care, long term care and behavioral health. Fortunately, Consumers and Clinicians agree on this, PWC reports that 56% of consumers and 30% of physicians are comfortable with data sharing for care coordination. Further progress is demonstrated by healthcare organizations willingly adopting cloud based applications –as of 2013, 40% of healthcare organizations were already storing protected health information (PHI) in the cloud.

Increased data access carries risk, leaving health data exposed, however. The threat of data breach or hacking is multiplied by the presence (in many cases necessary) of PHI on employee laptops and the fact that providers are provided increased access to PHI. Ponemon Institute, a security firm estimates that data breaches cost the industry $5.6 billion each year. Investments in data-centric security are necessary to assuage fear, protect personal health data and make secure data sharing a reality.

Early improvements in patient outcomes indicate that the relationship between data and healthcare is a valuable investment. The International Institute of Analytics supports this, reporting that although analytics and data maturity across healthcare lags other industries, the opportunity to positively impact clinical and operational outcomes is significant.

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Healthcare Consumer Engagement

The signs that healthcare is becoming a more consumer (think patients, members, Healthcareproviders)  driven industry are evident all around us. I see provider and payer organizations clamoring for more data, specifically data that is actionable, relatable and has integrity. Armed with this data, healthcare organizations are able to differentiate around a member/patient-centric view.

These consumer-centric views convey the total value of the relationships healthcare organizations have with consumers. Understanding the total value creates a more comprehensive understanding of consumers because they deliver a complete picture of an individual’s critical relationships including: patient to primary care provider, member to household, provider to network and even members to legacy plans. This is the type of knowledge that informs new treatments, targets preventative care programs and improves outcomes.

Payer organizations are collecting and analyzing data to identify opportunities for  more informed care management and segmentation to reach new, high value customers in individual markets. By segmenting and targeting messaging to specific populations, health plans generate increased member satisfaction and cost effectively expands and manages provider networks.

How will they accomplish this? Enabling members to interact in health and wellness forums, analyzing member behavior and trends and informing care management programs with a 360 view of members… to name a few . Payers will also drive new member programs, member retention and member engagement marketing and sales programs by investigating complete views of member households and market segments.

In the provider space, this relationship building can be a little more challenging because often consumers as patients do not interact with their doctor unless they are sick, creating gaps in data. When provider organizations have a better understanding of their patients and providers, they can increase patient satisfaction and proactively offer preventative care to the sickest (and most likely to engage) of patients before an episode occurs. These activities result in increased market share and improved outcomes.

Where can providers start? By creating a 360 view of the patient, organizations can now improve care coordination, open new patient service centers and develop patient engagement programs.

Analyzing populations of patients, and fostering patient engagement based on Meaningful Use requirements or Accountable Care requirements, building out referral networks and developing physician relationships are essential ingredients in consumer engagement. Knowing your patients and providing a better patient experience than your competition will differentiate provider organizations.

You may say “This all sounds great, but how does it work?” An essential ingredient is clean, safe and connected data.  Clean, safe and connected data requires an investment in data as an asset… just like you invest in real estate and human capital, you must invest in the accessibility and quality of your data. To be successful, arm your team with tools to govern data –ensuring ongoing integrity and quality of data, removes duplicate records and dynamically incorporates data validation/quality rules. These tools include master data management, data quality, metadata management and are focused on information quality. Tools focused on information quality support a total customer relationship view of members, patients and providers.

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Responsible Data Breach Reporting

databreachThis week, another reputable organization, Anthem Inc, reported it was ‘the target of  a very sophisticated external cyber attack’. But rather than be upset at Anthem, I respect their responsible data breach reporting.

In this post from Joseph R. Swedish, President and CEO, Anthem, Inc., does something that I believe all CEO’s should do in this situation.  He is straight up about what happened,  what information was breached, actions they took to plug the security hole, and services available to those impacted.

When it comes to a data breach, the worst thing you can do is ignore it or hope it will go away. This was not the case with Anthem.  Mr Swedish did the right thing and I appreciate it.

You only have one corporate reputation – and it is typically aligned with the CEO’s reputation.  When the CEO talks about the details of a data breach and empathizes with those impacted, he establishes a dialogue based on transparency and accountability.

Research that tells us 44% of healthcare and pharmaceutical organizations experienced a breach in 2014. And we know that when personal information when combined with health information is worth more on the black market because the data can be used for insurance fraud.   I expect more healthcare providers will be on the defensive this year and only hope that they follow Mr Swedish’s example when facing the music.

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Patient Experience-The Quality of Your Data is Important!

Patient_Experience

Patient Experience-Viable Data is Important!

Patient experience is key to growth and success for all health delivery organizations.  Gartner has stated that the patient experience needs to be one of the highest priorities for organizations. The quality of your data is critical to achieving that goal.  My recent experience with my physician’s office demonstrates how easy it is for the quality of data to influence the patient experience and undermine a patient’s trust in their physician and the organization with which they are interacting.

I have a great relationship with my doctor and have always been impressed by the efficiency of the office.  I never wait beyond my appointment time, the care is excellent and the staff is friendly and professional.  There is an online tool that allows me to see my records, send messages to my doctor, request an appointment and get test results. The organization enjoys the highest reputation for clinical quality.  Pretty much perfect from my perspective – until now.

I needed to change a scheduled appointment due to a business conflict.  Since I expected some negotiation I decided to make the phone call rather than request it on line…there are still transactions for which human to human is optimal!  I had all my information at hand and made the call.  The phone was pleasantly answered and the request given.  The receptionist requested my name and date of birth, but then stated that I did not have a future appointment.  I am looking at the online tool, which clearly states that I am scheduled for February 17 at 8:30 AM.  The pleasant young woman confirms my name, date of birth and address and then tells me that I do not have an appointment scheduled.  I am reasonably savvy about these things and figured out the core problem, which is that my last name is hyphenated.  Armed with that information, my other record is found and a new appointment scheduled. The transaction is completed.

But now I am worried. My name has been like this for many years and none of my other key data has changed.   Are there parts of my clinical history missing in the record that my doctor is using?   Will that have a negative impact on the quality of my care?  If I were to be unable to clearly respond, might that older record be accessed and my current medications and history not be available?  The receptionist did not address the duplicate issue clearly by telling me that she would attend to merging the records, so I have no reason to believe that she will.  My confidence is now shaken and I am less trustful of the system and how well it will serve me going forward.  I have resolved my issue, but not everyone would be able to push back to insure that their records are now accurate.

Many millions of dollars are being spent on electronic health records.  Many more millions are being spent to redesign work flow to accommodate the new EHR’s. Physicians and other clinicians are learning new ways to access data and treat their patients. The foundation for all of this is accurate data.  Nicely displayed but inaccurate data will not result in improved care or enhanced member experience.  As healthcare organizations move forward with the razzle dazzle of new systems they need to remember the basics of good quality data and insure that it is available to these new applications.

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