Category Archives: Healthcare
I have two kids. In school. They generate a remarkable amount of paper. From math worksheets, permission slips, book reports (now called reading responses) to newsletters from the school. That’s a lot of paper. All of it is presented in different forms with different results – the math worksheets tell me how my child is doing in math, the permission slips tell me when my kids will be leaving school property and the book reports tell me what kind of books my child is interested in reading. I need to put the math worksheet information into a storage space so I can figure out how to prop up my kid if needed on the basic geometry constructs. The dates that permission slips are covering need to go into the calendar. The book reports can be used at the library to choose the next book.
We are facing a similar problem (albeit on a MUCH larger scale) in the insurance market. We are getting data from clinicians. Many of you are developing and deploying mobile applications to help patients manage their care, locate providers and improve their health. You may capture licensing data to assist pharmaceutical companies identify patients for inclusion in clinical trials. You have advanced analytics systems for fraud detection and to check the accuracy and consistency of claims. Possibly you are at the point of near real-time claim authorization.
The amount of data generated in our world is expected to increase significantly in the coming years. There are an estimated 50 petabytes of data in the Healthcare realm, which is predicted to grow by a factor of 50 to 25,000 petabytes by 2020. Healthcare payers already store and analyze some of this data. However in order to capture, integrate and interrogate large information sets, the scope of the payer information will have to increase significantly to include provider data, social data, government data, pharmaceutical and medical product manufacturers data, and information aggregator data.
Right now – you probably depend on a traditional data warehouse model and structured data analytics to access some of your data. This has worked adequately for you up to now, but with the amount of data that will be generated in the future, you need the processing capability to load and query multi-terabyte datasets in a timely fashion. You need the ability to manage both semi-structured and unstructured data.
Fortunately, a set of emerging technologies (called “Big Data”) may provide the technical foundation of a solution. Big Data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage and process data within a tolerable amount of time. While some existing technology may prove inadequate to future tasks, many of the information management methods of the past will prove to be as valuable as ever. Assembling successful Big Data solutions will require a fusion of new technology and old-school disciplines:
Which of these technologies do you have? Which of these technologies can integrate with on-premise AND cloud based solutions? On which of these technologies does your organization have knowledgeable resources that can utilize the capabilities to take advantage of Big Data?
This 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.
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.
In a previous life, I was a pastry chef in a now-defunct restaurant. One of the things I noticed while working there (and frankly while cooking at home) is that the better the ingredients, the better the final result. If we used poor quality apples in the apple tart, we ended up with a soupy, flavorless mess with a chewy crust.
The same analogy can be applied to Data Analytics. With poor quality data, you get poor results from your analytics projects. We all know that companies that can implement fantastic analytic solutions that can provide near real-time access to consumer trends are the same companies that can do successful targeted marketing campaigns that are of the minute. The Data Warehousing Institute estimates that data quality problems cost U.S. businesses more than $600 billion a year.
The business impact of poor data quality cannot be underestimated. If not identified and corrected early on, defective data can contaminate all downstream systems and information assets, jacking up costs, jeopardizing customer relationships, and causing imprecise forecasts and poor decisions.
- To help you quantify: Let’s say your company receives 2 million claims per month with 377 data elements per claim. Even at an error rate of .001, the claims data contains more than 754,000 errors per month and more than 9.04 million errors per year! If you determine that 10 percent of the data elements are critical to your business decisions and processes, you still must fix almost 1 million errors each year!
- What is your exposure to these errors? Let’s estimate the risk at $10 per error (including staff time required to fix the error downstream after a customer discovers it, the loss of customer trust and loyalty and erroneous payouts. Your company’s risk exposure to poor quality claims data is $10 million a year.
Once your company values quality data as a critical resource – it is much easier to perform high-value analytics that have an impact on your bottom line. Start with creation of a Data Quality program. Data is a critical asset in the information economy, and the quality of a company’s data is a good predictor of its future success.
Every year, I get a replacement desk calendar to help keep all of our activities straight – and for a family of four, that is no easy task. I start with taking all of the little appointment cards the dentist, orthodontist, pediatrician and GP give to us for appointments that occur beyond the current calendar dates. I transcribe them all. Then I go through last year’s calendar to transfer any information that is relevant to this year’s calendar. And finally, I put the calendar down in the basement next to previous year calendars so I can refer back to them if I need. Last year’s calendar contains a lot of useful information, but no longer has the ability to solve my need to organize schedules for this year.
In a very loose way – this is very similar to application retirement. Many larger health plans have existing systems that were created several years (sometimes even several decades) ago. These legacy systems have been customized to reflect the health plan’s very specific business processes. They may be hosted on costly hardware, developed in antiquated software languages and rely on a few developers that are very close to retirement. The cost of supporting these (most likely) antiquated systems can be diverting valuable dollars away from innovation.
The process that I use to move appointment and contact data from one calendar to the next works for me – but is relatively small in scale. Imagine if I was trying to do this for an entire organization without losing context, detail or accuracy!
There are several methodologies for determining the best strategy for your organization to approach software modernization, including:
- Architecture Driven Modernization (ADM) is the initiative to standardize views of the existing systems in order to enable common modernization activities like code analysis and comprehension, and software transformation.
- SABA (Bennett et al., 1999) is a high-level framework for planning the evolution and migration of legacy systems, taking into account both organizational and technical issues.
- SRRT (Economic Model to Software Rewriting and Replacement Times), Chan et al. (1996), Formal model for determining optimal software rewrite and replacement timings based on versatile metrics data.
- And if all else fails: Model Driven Engineering (MDE) is being investigated as an approach for reverse engineering and then forward engineering software code
My calendar migration process evolved over time, your method for software modernization should be well planned prior to the go-live date for the new software system.
I live in a very small town in Maine. I don’t spend a lot of time thinking about my privacy. Some would say that by living in a small town, you give up your right to privacy because everyone knows what everyone else is doing. Living here is a choice – for me to improve my family’s quality of life. Sharing all of the details of my life – not so much.
When I go to my doctor (who also happens to be a parent from my daughter’s school), I fully expect that any sort of information that I share with him, or that he obtains as a result of lab tests or interviews, or care that he provides is not available for anyone to view. On the flip side, I want researchers to be able to take my lab information combined with my health history in order to do research on the effectiveness of certain medications or treatment plans.
As a result of this dichotomy, Congress (in 1996) started to address governance regarding the transmission of this type of data. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a Federal law that sets national standards for how health care plans, health care clearinghouses, and most health care providers protect the privacy of a patient’s health information. With certain exceptions, the Privacy Rule protects a subset of individually identifiable health information, known as protected health information or PHI, that is held or maintained by covered entities or their business associates acting for the covered entity. PHI is any information held by a covered entity which concerns health status, provision of health care, or payment for health care that can be linked to an individual.
Many payers have this type of data in their systems (perhaps in a Claims Administration system), and have the need to share data between organizational entities. Do you know if PHI data is being shared outside of the originating system? Do you know if PHI is available to resources that have no necessity to access this information? Do you know if PHI data is being shared outside your organization?
If you can answer yes to each of these questions – fantastic. You are well ahead of the curve. If not – you need to start considering solutions that can
- Identify PHI in all of your data streams
- Monitor and track the flow of this data throughout your organization and
- Mask this data if it is being shared with resources that don’t need to be able to identify the individual.
I want to researchers to have access to medically relevant data so they can find the cures to some horrific diseases. I want to feel comfortable sharing health information with my doctor. I want to feel comfortable that my health insurance company is respecting my privacy. Now to get my kids to stop oversharing.
I understand that fighting for budget and time to implement analytics is a challenge with all the changes happening in healthcare (ICD-10, M&A, etc.). But hospitals using analytics to drive Value-based care are leading healthcare reform and setting a higher bar for quality of service. Value-based care promises quicker recoveries, fewer readmissions, lower infection rates, and fewer medical errors – something we all want as consumers.
In order to truly achieve value-based care, analytics is a must have. If you are looking for the business case or inspiration for the business driver, here are a few ideas:
- In surgery, do you have the data to show how many patients had lower complication rates and higher long-term survival rates? Do you have that data across the different surgical procedures you offer?
- Do you have data to benchmark your practice quality? How do you compare to other practices in terms of infection rates? Can you use that data to promote your services from a marketing perspective?
- Do you know how much a readmission is costing your hospital?
- From a finance perspective, have you adopted best practices from other industries with respect to supply-chain management or cost optimization strategies?
If you don’t have the expertise, there are plenty of consulting organizations who specialize in implementing analytics to provide insight to make the transition to value-based care and pricing.
We are always going to be facing limited budgets, the day will always have 24 hours in it, and organizations are constantly changing as new leaders take over with a different agenda. But one thing is certain; a decision without data is just someone’s opinion. In healthcare with only half of the executives making decisions based on analytics, maybe we should all be asking for a second opinion – and one based on data.
However, there is another wearable that has my attention – a wearable designed to save children’s lives: Embrace. Embrace is the first medical-quality wearable to help measure stress, epileptic seizures, activity and sleep. The idea is it an be used to detect early signs of an event and alert you when an unusual event is about to happen. If you have a toddler or infant, the wearable could alert parents in the middle of the night. As a mother of four children, peace of mind in the night is king.
Imagine the possibilities.
Biometric data collected from devices like these when used in the classroom could be used as a predictor children with Autism, Asperger Syndrome, or Mood Disorders to help clinicians, educators and parents better understand when a child is starting to become dis regulated. Integrating that data with therapeutic and educational strategies could potentially provide insight into a practice that is largely trial and error.
I pledged my support for Embrace in hopes that innovation in this field will continue to prosper, saving lives, and ultimately making a difference in the world.
At the DIA conference in Berlin this month, Frits Stulp of Mesa Arch Consulting suggested that IDMP could get the business asking for MDM. After looking at the requirements for IDMP compliance for approximately a year, his conclusion from a business point of view is that MDM has a key role to play in IDMP compliance. A recent press release by Andrew Marr, an IDMP and XEVMPD expert and specialist consultant, also shows support for MDM being ‘an advantageous thing to do’ for IDMP compliance. A previous blog outlined my thoughts on why MDM can turn regulatory compliance into an opportunity, instead of a cost. It seems that others are now seeing this opportunity too.
So why will IDMP enable the business (primarily regulatory affairs) to come to the conclusion that they need MDM? At its heart, IDMP is a pharmacovigilance initiative which has a goal to uniquely identify all medicines globally, and have rapid access to the details of the medicine’s attributes. If implemented in its ideal state, IDMP will deliver a single, accurate and trusted version of a medicinal product which can be used for multiple analytical and procedural purposes. This is exactly what MDM is designed to do.
Here is a summary of the key reasons why an MDM-based approach to IDMP is such a good fit.
1. IDMP is a data Consolidation effort; MDM enables data discovery & consolidation
- IDMP will probably need to populate between 150 to 300 attributes per medicine
- These attributes will be held in 10 to 13 systems, per product.
- MDM (especially with close coupling to Data Integration) can easily discover and collect this data.
2. IDMP requires cross-referencing; MDM has cross-referencing and cleansing as key process steps.
- Consolidating data from multiple systems normally means dealing with multiple identifiers per product.
- Different entities must be linked to each other to build relationships within the IDMP model.
- MDM allows for complex models catering for multiple identifiers and relationships between entities.
3. IDMP submissions must ensure the correct value of an attribute is submitted; MDM has strong capabilities to resolve different attribute values.
- Many attributes will exist in more than one of the 10 to 13 source systems
- Without strong data governance, these values can (and probably will be) different.
- MDM can set rules for determining the ‘golden source’ for each attribute, and then track the history of these values used for submission.
4. IDMP is a translation effort; MDM is designed to translate
- Submission will need to be within a defined vocabulary or set of reference data
- Different regulators may opt for different vocabularies, in addition to the internal set of reference data.
- MDM can hold multiple values/vocabularies for entities, depending on context.
5. IDMP is a large co-ordination effort; MDM enables governance and is generally associated with higher data consistency and quality throughout an organisation.
- The IDMP scope is broad, so attributes required by IDMP may also be required for compliance to other regulations.
- Accurate compliance needs tracking and distribution of attribute values. Attribute values submitted for IDMP, other regulations, and supporting internal business should be the same.
- Not only is MDM designed to collect and cleanse data, it is equally comfortable for data dispersion and co-ordination of values across systems.
Once business users assess the data management requirements, and consider the breadth of the IDMP scope, it is no surprise that some of them could be asking for a MDM solution. Even if they do not use the acronym ‘MDM’ they could actually be asking for MDM by capabilities rather than name.
Given the good technical fit of a MDM approach to IDMP compliance, I would like to put forward three arguments as to why the approach makes sense. There may be others, but these are the ones I feel are most compelling:
1. Better chance to meet tight submission time
There is slightly over 18 months left before the EMA requires IDMP compliance. Waiting for final guidance will not provide enough time for compliance. Using MDM you have a tool to begin with the most time consuming tasks: data discovery, collection and consolidation. Required XEVMPD data, and the draft guidance can serve as a guide as to where to focus your efforts.
2. Reduce Risk of non-compliance
With fines in Europe of ‘fines up to 5% of revenue’ at stake, risking non-compliance could be expensive. Not only will MDM increase your chance of compliance on July 1, 2016, but will give you a tool to manage your data to ensure ongoing compliance in terms of meeting deadlines for delivering new data, and data changes.
3. Your company will have a ready source of clean, multi-purpose product data
Unlike some Regulatory Information Management tools, MDM is not a single-purpose tool. It is specifically designed to provide consolidated, high-quality master data to multiple systems and business processes. This data source could be used to deliver high-quality data to multiple other initiatives, in particular compliance to other regulations, and projects addressing topics such as Traceability, Health Economics & Outcomes, Continuous Process Verification, Inventory Reduction.
So back to the original question – will the introduction of IDMP regulation in Europe result in the business asking IT to implement MDM? Perhaps they will, but not by name. It is still possible that they won’t. However, for those of you who have been struggling to get buy-in to MDM within your organisation, and you need to comply to IDMP, then you may be able to find some more allies (potentially with an approved budget) to support you in your MDM efforts.