Big Data is being touted as the panacea that will solve current challenges as government reforms create pressure to improve the quality of healthcare while decreasing costs.  Words like volume, velocity, variety and veracity are often used to talk about Big Data.  However, in the purest business sense, Big Data is primarily about validity and value. In healthcare in particular, one should be mindful that Big Data can very well turn into big confusion or big mistakes — especially if the data being queried does not relate to the question being asked.

How do we measure the value of data?
The size of the data is often not as critical as one’s ability to identify specific data points that, when appropriately aggregated and manipulated, deliver valuable, meaningful, and actionable insights.  Knowing what to do with this knowledge and executing on those things is the key to unlocking value —  big or otherwise. Process and operational changes that improve efficiency, quality or both must be made in order to realize a return on investment (ROI) on data.   However, before healthcare administrators or clinicians will take action, they need trust that the decisions they’re making are based on accurate information.

How do we separate usable data from noise?
Data in and of itself is not valuable, and, more does not always equal better.  In fact, collecting vast amounts of superfluous data can make deriving insights more complicated. In any given data, there is noise that makes interpreting the signal difficult. The bigger the data, the louder the noise. But what is noise? Noise is how we describe certain data within a data set.  A data set is made up of data points that are related to each other in some way.  Although the data points are related to each other, individual data points themselves may not be relevant to the question at hand. 

Identifying relevant data
Data is usable and trustworthy based on its validity or ability to accurately represent a phenomenon, be it processes of care, treatment regimes, care giver activity or patient symptoms.  Therefore, data points are usable within a specific context. The context of the data – who collected it, where it was collected, when it was collected, and how it was collected — is all relevant in selecting the most valid data for analysis. Given the breadth and complexity of patient care and healthcare operations it is no surprise that many organizations do not truly know what kind of data – or how much of it – they need to archive or collect in order to generate actionable insights from their information.

It takes years of experience working in the industry to understand patient care related processes and operations.  This type of knowledge and insight is critical to understanding the context of healthcare data (the what, where, when and how).  A greater understanding of the data context enables one to provide better inputs, which leads to better outputs. Better outputs lead to better questions and answers. Better answers lead to knowledge and understanding.

In future posts, I’ll share some of the least considered ‘contexts’ for the most common healthcare data sets. These perspectives have made a difference in hundreds of database designs, healthcare analyses and improvement initiatives.

Diagnoses/Problems
Diagnoses and problems are regularly used to identify cohorts of patients. To understand how a disease process is impacting a patient population, start by identifying the patients with the diagnosis of interest.  Sounds simple right? It may or may not be, depending on the context of the question being asked. Below are 6 considerations that are useful when determining the suitability of data points to include with diagnoses and problems.

• Where – diagnoses can be found in encounters, orders, medical histories, and problem lists to name a few. Each of these locations represents a slightly different context for this information. Be sure to know which source is the most relevant to the question being asked.
• Status, whether chronic or acute, may be relevant depending on the question.  There is a big difference between evaluating quality of care for patients with acute bronchitis versus those with chronic bronchitis (often associated with COPD).
• Specificity of diagnosis may be an important consideration – for example, when analyzing diabetic populations, is it appropriate to include gestational diabetics in the cohort?
• Temporality – the initial diagnosis date may be relevant, as well as the date the problem was resolved, depending on the time frame of interest.  This is often important in determining which patients to include in the look-back period of a reporting year.
• Order – Whether a diagnosis is the primary or secondary assigned to an encounter may also be an important consideration. This is especially true when using diagnoses from claims data.
• Reason for Visit may provide relevant information in addition to encounter diagnosis when forecasting future utilization.
• Diagnoses associated with physician procedures or orders may provide valuable information for identifying differential diagnoses patterns or trends.

We’ll continue to explore the different contexts of data sets that offer the biggest bang for your buck. Whether deciding what data to archive, what to include in a data warehouse, what to use in developing predictive analytics, or what to feed an analytics product, data relevance and validity is critical to realizing ROI for health IT investments.

CETA and Harmony Healthcare IT solve complex data challenges on a regular basis. If you need best practice recommendations or consultation to define a data strategy, please contact us.

Guest Blog submitted by Michelle Currie, RN, MSN, CPHIMS, CPHQ

Michelle is a registered nurse with more than 15 years of experience in Clinical Informatics. She received her BSN from the University of Michigan and her MSN in Clinical Informatics from the University of California at San Francisco.  In addition to serving as Chief Data Officer at CETA, Michelle volunteers her time as an annual Conference Presentation Reviewer, Mentor and Moderator for HIMSS.

Editor’s Note: This blog has been updated from an earlier version posted on June 25th, 2015.

Healthcare providers deal with HIPAA privacy and security issues every day. Staff members follow strict processes to ensure compliance with how Electronic Protected Health Information (ePHI) is stored both on paper and electronically. It also is essential to consider HIPAA data retention regulations when archiving ePHI after decommissioning a system.

In his article “The impact of HIPAA and HITECH on healthcare data governance” published in Management Health Technology magazine, Chris Grossman says that health practices must prioritize data governance for data that is both stored and in transmission. “The system must demonstrate proactive compliance, and healthcare organizations must be able to demonstrate that their everyday transactional, back-up and storage processes actively preserve patient information security,” writes Grossman.

In an effort to prevent fines for non-compliance, both small medical practices and larger healthcare facilities need to consider how the method in which they archive legacy EHR will meet HIPAA data retention regulations.

Consider the following ePHI archive HIPAA safeguards when storing data long-term:

Access Levels

When patient data is output to paper or PDF, it is exposed in its entirety to anyone who gains access to the document.  That document may contain sensitive information like financial records and clinical or behavioral health data.  While it is possible for a network administrator to make separate PDF files available for the access level of each staff member, this strategy creates additional work for the IT department as well as introduces the possibility for manual errors in the data separation process.

With old EHR archiving, user-based access level mapping allows you to limit each staff member’s access to specific sets of patient information relevant to their job. Sensitive information can only be viewed by authorized users.

Access Tracking

Once the legacy EHR is archived, HIPAA requires that an audit log of access to the records be maintained.  When a staff member accesses a paper or PDF document, it may be more difficult to log an exact date and time of the access much less which portion of the record was viewed, copied or printed. While third-party audit logging software may be used to track access to the ePHI archive, it may not always provide a complete picture of an employee’s activity.

When EMR migration and archiving is implemented, tracking software is often integrated right into the archive solution to allow compliance officers to view archive access audit logs. Any log should contain a user ID, date and time stamp and a summary of the information viewed, printed or exported.

Data Encryption

HIPAA requires that electronic PHI is encrypted with the use of an algorithmic process to transform data into a form in which there is a low probability of assigning meaning without use of a confidential process or key – (45 CFR 164.304 definition of encryption). If patient data is archived using paper or PDF documents it becomes more difficult to secure or encrypt specific portions of the chart.

When selecting a legacy EHR archiving solution, make sure that the product uses a HIPAA-compliant format via data encryption, Secure Sockets Layer (SSL) connectivity, security access level mapping and audit logging.  Archived data should also be encrypted while at rest.

How have you addressed HIPAA compliance during the archival of your protected health information?

Editor’s Note: This blog has updated from an earlier post in 2015.

It is essential that health organizations comply with medical record retention laws both in their state and with any licensing agencies. Non-compliance can result in consequences, including fines and increased legal liability. By taking the time to create an effective medical record retention policy, you reduce risk for non-compliance and secure protected health information (PHI) so it’s easily accessible as inquiries from patients, payers, auditors, law firms and other entities are fielded in years to come.

Here are some tips for helping to create a medical record retention policy:

• Determine how long you must retain medical records. While HIPAA requires health organizations to retain records for six years from the date of creation or the date the record was last in effect, some states and other agencies may require records to be retained for a longer period of time. Check your state requirements as well as any accrediting agencies for the impact on your organization. Note that Medicare managed care providers are required to retain records for ten years. Follow the guidelines for health information retention and archiving law that require you to retain records for the longest period of time. A good resource for record retention guidelines is the American Health Information Management Association’s (AHIMA) recommendation for retention.

• Document your medical record retention and archiving policy. Documentation of the policy is important for both succession purposes and emergencies when the primary health information management staff is unavailable. Be sure to include the process by which medical records will be retained in your organization’s official policy (i.e., process for storing records in paper format as well as in scanned or discrete data element format). Sample medical records management and retention policies can be found in an online search.

• Evaluate options available for electronic medical record retention. Many health organizations opt to electronically archive scanned images and/or discrete health data elements from historical patient records.  This usually involves a data extraction from a legacy health application and a migration of that data into a secure relational database.  A typical health data archive includes a front end user interface that allows easy access for viewing historical patient records.  This is different from a backup of the historical data which makes it more difficult to access information on-demand.  Electronic medical record retention into an archive is a great option when one hospital information system, electronic health record, practice management system or any other healthcare application containing PHI is replaced with another.  Scanned paper documents may also be stored in such an archive. As it is essential to adhere to HIPAA regulations when archiving legacy electronic patient data, consider HIPAA data retention safeguards.

• Review your documented medical record retention policy with legal counsel. Due to the number of variables affecting the length of time a provider should keep a medical record, it is recommended that your retention policy gets reviewed by legal counsel.  An attorney will be able to validate federal and state laws as well as medical board and state association or agency policies.  They may also offer guidance on policies regarding the destruction of records.

• Train your staff on the procedures for both retaining and accessing medical record data. Your retention strategy is only effective if the information is secure and the necessary personnel can quickly access the records when needed. Train all relevant employees on the process for accessing retained medical data and update the employee handbook with the guidelines health information retention and archiving.

Is a documented medical record retention policy in place for your organization? If so, when is the last time it was reviewed? 

Editor’s Note: This blog has been updated from an earlier post on February 26th, 2015.

Mergers and acquisitions in US healthcare remain strong for the fourteenth quarter in a row for the beginning of 2018, reports PricewaterhouseCoopers. With 200 deals reported in the first quarter of 2018, healthcare organizations continue to shift and consolidate. With M&A activity also comes data migration as data from newly acquired providers needs to be thoughtfully integrated into the organization. However, there are some considerations to ensure there is a solid marriage of new and existing data.

Without the right resources, moving EMR information from one database to another can be risky, costly and time-consuming.  As healthcare organizations don’t typically have internal resources at the ready to perform legacy electronic medical data migrations themselves, they seek conversion services from their EMR vendor or a third-party.  Here are some medical data migration tips to ensure data integrity, minimize expenses and hit targeted deadlines.

• Partner with a firm specializing in clinical data migration. Critical to the success of the project is working with a vendor who focuses solely on EHR data migration and extractions and understands the nuances of clinical code sets.  An EMR vendor knows its own database structure inside and out, but, may not be as intimately familiar with the source EMR.  This can be problematic, sandwiching the health organization between the vendors should the source EMR vendor need to be engaged.  Finding a vendor-neutral service provider who has worked with both EMR systems should reduce risk and increase project efficiency.  Be sure to ask the service provider for references and make calls to ensure their “like” migration projects met budgets and timelines with no major obstacles.

• Assemble a cross-functional data governance team While the technical aspects of clinical migration fall to the IT department, data scoping, validation and quality assurance fall largely to business stakeholders. Appoint a cross-functional team of IT analysts and software users who will collectively weigh in on the electronic medical data migration plan.  Make sure the right HIM resources are at the table to answer questions about what data you have and don’t have as well as what data is highly critical versus less so. Determine how data will be tested, evaluated and approved.  Identify a manager for setting the project in motion and a sponsor for providing ultimate sign-off.

• Define the scope of EMR data to be migrated. The more clinical data you migrate, the more complex (and therefore the more costly) the project.  Determining what legacy EMR data to migrate versus archive is a key to controlling costs. Ask your data governance team:  which data points, from how far back, are critical to present at point of care? Once this clinical summary data set is identified, the rest of the historical information — including financial transactional history — can be secured as discrete and searchable data elements in a long-term, HIPAA-compliant EMR archive. Note:  while the destination EMR will likely accept demographics, it may limit the type or amount of clinical data accepted.  If this is the case, look into feeding clinical document architecture (CDA) summaries to the destination EMR via its portal.

• Identify where data from the source EMR will reside in the destination EMR. Third-party vendors don’t always provide an actual demonstration or screen mock-up of where and how the source data will appear in the destination EMR.  Fully understanding the end result data placements and workflows is critical so that expectations — and ultimately timeframes and costs — are met. If it isn’t right the first time, a greater level of effort and time is needed the second and even third time around.  Data mapping and visualization is tedious but necessary, so, negotiate it into the contact as a deliverable.

• Document the data mapping for future reference. The logic and database structure of the source EMR is likely vastly different from the logic and database structure of the destination EMR.  If one system calls source ID “SID” and the other system calls source ID “SourceID” then the two must be mapped to minimize redundancy and reduce errors.  Data mapping is expected, but, its formal documentation isn’t always provided by a vendor as a deliverable. Documenting the data mapping efforts as the project progresses makes each record traceable.  That means that as breaks in logic or issues in quality occur, they can be quickly corrected.  Think of your data mapping documentation as a quick and easy roadmap back to a source EMR field in question.  A lot of time and effort can be saved by formally documenting the data mapping.

• Determine the environment in which the clinical data migration will take place. Will the migration take place onsite or offsite?  It is important to consider the equipment, resources and infrastructure required for both.  If it will occur onsite:  How long will it take? How might the network be taxed or degraded as the data is moving?  It may take some math to throttle the throughput so the network is not crippled for your users. If it occurs offsite:  How will the vendor gain access to the data? What is the process? How will privacy and security be addressed?

• Collaborate with your vendor on a data access and validation process. The two areas where a medical data migration can drag out past deadlines is in the beginning and at the end. In the beginning, the healthcare organization needs to make the data accessible to the vendor, getting the right authorization logistics in place and then successfully granting system connection rights.  A word to the wise: this can never begin too early because it inevitably seems to take longer than expected due to scheduling and resource issues. In terms of data validation, allot an appropriate amount of time for it in the project plan.  A second word to the wise: the amount of time allotted for data validation is directly proportionate to the quality outcome or lack thereof of said data.  In other words, allow for several iterations. No matter how experienced a vendor is, every system is different and, as a result, you typically don’t get 100% validation on the very first-pass. Utilize your user acceptance testing (UAT) environment.

In conclusion, most healthcare organizations do not have the time or ability to conduct clinical data migrations exclusively on their own, and, most EMR vendors over estimate their ability to migrate legacy clinical data.  Best practice might be to assign a project manager and put out an RFP for Medical Data Storage to find an experienced legacy EHR migration service provider who can keep risk, cost and timing in check. This approach can expedite the process by allowing key data to be migrated while also archiving all data necessary to meet medical record retention policies.

Editor’s Note: This blog contains content from an earlier post on May 9th, 2017.

Each healthcare merger and acquisition transaction is different. One size does not fit all when it comes to consolidating practices, hospitals or other types of healthcare facilities – especially where technology is concerned.

In his article “Post Merger Best Practices for Healthcare Organizations,” John Hesselmann details several EMR technology transition best practices to help navigate mergers in the most efficient manner. He recommends establishing what success looks like by defining measurement benchmarks as well as being transparent and assigning accountability.  Hesselmann also recommends thinking long-term about technology.

“When making a decision, examine the lifecycle of the present IT systems of the two companies, as well as what the costs will be to maintain two separate systems vs. upgrading to one system,” writes Hesselmann. Because of the multitude of transitional challenges and costs inherent with a merger, it can be easy to put the technology decisions off. But, doing so may cost money and liability in the long-run.

The cost of technology in a healthcare merger

Technology is expensive. It’s not just purchasing new equipment but the cost to maintain and administer hardware and software as well. In the case of a merger or acquisition, EMR technology is often inherited from the newly-joined entity.  Suddenly, the cost of maintaining two systems hits the bottom line. Many healthcare organizations find that the most cost effective solution is to consolidate the infrastructure, implementing a single enterprise-wide solution for providers and staff. Data from the legacy system is then often migrated to a health care archive for long-term storage of protected health information to meet medical record retention requirements.

The liability of technology in a healthcare merger

As it relates to record retention, mergers and acquisitions also often present an opportunity to evaluate liability. Many compliance officers overlook how technology can contribute to liability, especially the way in which patient or employee records are stored long-term. There are risks and costs associated with storing records on a legacy system for the long haul.  If the legacy system fails or the maintenance contract is allowed to lapse, then patient or employee records may not be available to access if requested during litigation. This may result in a settlement that could’ve otherwise been avoided had the proper data been provided. By considering the liability issues in technology decisions during the merger process, providers can reduce both the stress and financial impact of potential litigation issues.

One of the biggest mistakes that healthcare organizations can make during a merger or acquisition is to minimize, delay or ignore evaluating the impact of technology as two entities and system environments are consolidated into one. While it may seem that keeping a legacy system up and running as an archive is the most convenient solution, it is essential to consider the long term impact of that decision on cost and liability. Many CIOs explore extracting data from one system and migrating key elements to the go-forward system. They then store the balance of the historical records in a separate archive solution. This allows for easy access to patient or employee records long-term for e-discovery. By taking the time at the onset of the merger to evaluate technology, healthcare organizations can save time, money and headache in the future.

How is your organization handling legacy healthcare systems in its IT portfolio? Does your M&A team have a strategy for handling the consolidation of technology when a practice or hospital merger occurs?

Editor’s Note: This blog has been updated from an earlier post in 2015.

Health Informatics Demand Growing at Twice the Rate of Employment

Healthcare reform depends on better management of medical information. That’s why the HITECH Act, part of the 2009 American Recovery and Reinvestment Act (ARRA), provided funds to establish the Health IT Workforce Development Program.  Yet, a 2014 report from Burning Glass tells us the labor market for health informatics is not keeping up with the demand. In fact, they report the demand for health informatics workers is projected to grow at twice the rate of employment overall.

Medical Software Retirement Creating New Health Informatics Roles

That’s no surprise.  When HITECH incentives were announced, electronic health record (EHR) implementations took off so providers could hit meaningful use objectives.  Today, we see providers retiring their original EHR and medical billing systems due to dis-satisfaction, cost, end of life or merger and acquisition. Ironically, the decommissioning of these medical systems is actually creating new roles for health informaticists.  Why?  Because with each EHR system replacement, volumes of health data must be migrated or archived to ensure compliance with medical record retention laws. The strategy and tactics associated with legacy data management are spawning new job responsibilities and titles.

Job Titles Focused on Legacy Data Management Emerging

A quick search in the HIMSS JobMine for positions that mention “legacy” as a keyword reveals a screen full of active positions ranging from job titles like Solution Architect to Clinical Informaticist to Data Integration or Technical Analyst.  In our day-to-day work involving health data archiving and EMR storage, we now find ourselves working with consultants who specialize in helping organizations archive patient data.  We see new job titles such as Data Archive Analyst, Legacy Data Specialist and Legacy Systems Project Manager.  Most of these positions seem to be reporting into Directors and Managers of Applications, Interoperability Managers or even directly to the CIO.

Tiffany Crenshaw, President and CEO of Intellect Resources, a firm specializing in healthcare IT recruiting, confirms our findings.  “New roles and responsibilities focused on legacy management are certainly emerging,” Crenshaw says.  “These jobs tend to be appointed internally, however, taken on by the more seasoned IT resources who were deeply involved with the database structures of the legacy systems. While we have seen a slight uptick in requests to provide talent that is capable of supporting legacy systems, firms like ours are still primarily filling implementation and support roles for the go-forward EMR.”

Whether these legacy data management job roles are filled internally or from the outside, they are still contributing to the overall industry growth.  The great news is that whether you’re new to the health informatics field or a veteran, there seems to be a place for you to login each day and help to ensure the effective use of information that supports the safe and effective delivery of healthcare.

Has your healthcare organization created new jobs focused on legacy data management?  If so, what are some of the job titles?

Editor’s Note: This blog contains information from an earlier blog on July 8th, 2015.

One theme that health IT consultant Mike Burger sees at outpatient clinics is frustration with the current electronic health record (EHR), mostly driven by the fact that the practice has outgrown the system it has relied on for years. While system replacement often is viewed as the solution, the challenge of handling years-worth of legacy data that must be retained is still an issue.

When advising organizations of their next steps, Mike Burger recommends archiving legacy data. As a result, the data is still available, easily accessible, and stored in a secure and searchable format. Archiving allows the organization to easily move on to its new EHR system without having to deal with a tedious and costly data conversion.

Click here to watch a short video featuring Mike Burger at HIMSS18, weighing in on the topics of EHR system replacement and archiving.

Harmony Healthcare IT works with medical practices of all sizes and in various states of planning for legacy data retention. We have a streamlined and seamless process that removes the burden of how to manage historical records while meeting record retention mandates.

Contact the legacy data management specialists at Harmony Healthcare IT to learn more about medical record retention tools like HealthData Archiver^®.

About Mike Burger: Mike Burger is a Senior Consultant at Point-of-Care Partners, a leading management consulting firm that specializes in assisting healthcare organizations to evaluate, develop, and implement winning health information management strategies. With 25+ years of experience, Mike has become a specialist in Electronic Health Records (EHR), Electronic Data Interchange (EDI) and Health Information Exchange (HIE).

Healthcare merger and acquisition activity is prevalent as hospitals and health insurance companies look at growth and alliances to drive success in a post-healthcare-reform world. But with M&A comes real-world data sharing and EHR conversion problems as it pertains to information systems and electronic health records.  Successfully merging data from multiple sources is, perhaps, one of the most misunderstood and consistently underestimated problems in health IT today.

Harmonizing EHR Data to be Usable Outside of its Source Systems

If you are combining data from multiple sources and want the data to be usable outside of the source system then you will need to merge the data into a harmonized database or dataset.  A “harmonized” dataset offers the benefit of storing data from different systems in a discrete and re-usable form for extraction and import to a reporting or other EHR system. In converting to new EMR, the process involves taking data from a legacy system and doing the best you can to map and “fit” the data into a new system. It may sound simple, but it’s not as easy as connecting the pipes, turning on the water, and getting drinking water out the other end.

What Makes EHR Data Harmonization Difficult? 

What makes data harmonization in healthcare difficult is not the volume, variation, or other “V’s” of Big Data (although they do present their own unique challenges).  Data harmonization in healthcare is difficult because of the high level of ambiguity and complexity in the data concepts themselves. For example, patient demographic information can be merged fairly easily from one system to another. Demographic concepts (age, gender, address, etc.) are highly certain, or unambiguous.  This means there is general agreement as to what “age” means in relation to a patient.  It’s easy to identify the field for age in a legacy system and map it to the field for age in the go-forward system. There may be formatting issues at play, MMDDYYYY versus YYYYMMDD for example, but everyone is operating under the same definition for the data point itself.  But, the certainty ends there. Once you get past the simplicity of demographic data, you quickly find that financial and clinical data points aren’t as easy to map between systems for a multitude of reasons.

The Challenges of Mapping Clinical and Financial Data for Conversion

First, there is a high level of ambiguity surrounding clinical and financial data.  Much of this ambiguity stems from the lack of standardization in healthcare practices, processes and payment. Recall any recent article about how healthcare prices are determined — the seeming lack of rhyme or reason —  and you can easily see where inconsistency in practices leads to ambiguity of the concepts themselves. Second, EHR conversion is challenging because not all systems store data in the same discrete format, and many systems use proprietary logic that determines how and where data is stored in the underlying database. Third, the flexibility of commercial EHRs has been both a blessing and a curse. Many organizations chose to customize their EHRs to “fit” local operations, realizing too late that they had opted out of a standardized workflow that would have collected data in a clean, consistent, and re-usable way.  Some missed the opportunity to critically evaluate business as usual, automating bad business practices that led to bad data collection. Other organizations made poor design choices inserting mandatory fields at inopportune times during patient care that led to workarounds and bad data capture.  Local EHR configuration and workflows directly determine how data is captured, and ultimately, the interoperability, quality and re-usability of the data for other purposes.

Six Healthcare Data Considerations When Replacing an EHR

In our work with hundreds of acute and ambulatory healthcare organizations, we’ve identified six health data migration considerations when replacing one EHR with another.

1. Filtering Patient Records. This is a method for filtering out certain patients to exclude from migration. For example, you may not want to migrate patients who are deceased or inactive. Is there a deceased patient flag in the system? If so, can it be trusted? If the plan is to move recent patients only, then what criterion will be used to filter those patients? Will you use last appointment date, encounter date or charge date? It is important to validate the integrity of your healthcare data prior to migration.
2. Starting with a Clean Slate. Demographics are, of course, a must-migrate. Beyond that, the ability or cost to logically map detailed collections of data from one system to another can become a limiting factor. For example, it is sometimes difficult to neatly convert insurance plan identifiers between two databases. So, while it may entail a lot of manual entry, many providers use the new EHR system implementation as a trigger point to re-collect insurance information from patients at check-in and start fresh entering it manually into the new EHR.
3. Matching Patients. If you are migrating health data to a system that is already in use (as opposed to a brand new system that is not yet in use) and there is not a common identifier between the current EHR and the go-forward EHR, a patient matching event should certainly occur. First, clean-up duplicate patient accounts on the current system. Next, determine which fields to use for matching logic. Common fields to match off of include: First Name, Last Name, Middle Name/Initial, DOB and SSN. You can take it one step further by also matching on street address.

Where do you set the bar for a patient match? Think about how many fields need to matchup to be considered a true match: 5 out of 5? 4 out of 5? Perhaps you will schedule a manual review at 3 out of 5 and 4 out of 5? You also need to decide if anything less than 2 out of 5 matches are automatically marked as a fail.

For patients who do not have a match in the go-forward system, should a new master patient identifier (MPI) be assigned? If the MPI is sent over empty, will the go-forward system create the MPI on the fly? Determine if an MPI should be assigned prior to that patient being migrated to the go-forward system and set a standard to follow when generating that MPI:

• How many digits should it be?
• What schema will it follow? (i.e., 10xxxx or ABCxxx)
• Most importantly, will that new MPI play nicely with identifiers in systems interfaced with the go-forward system? For example, when migrating data from CPSI to Cerner, let’s say that MRNs for unmatched patients were auto-generated with no standards. If that were the case then once the site was live on Cerner, it stands to reason that they could realize some of the identifiers used were MRNs in their PACS system that was interfaced with Cerner. That could entail data from a patient in Cerner being sent over to a different patient MRN in the PACS system. This is why pre-planning is critical.

4. Timing it Out. How long will it take for the final data pull to be done and pushed into the new system? The answer requires some decision making and some math. Determine if you will utilize a differential or do dual entry. Depending on data content and size, estimate the time it will take (typically 1-2 weeks). Planning ahead and allow ample time for the healthcare data migration can avoid some headaches for the rest of the EHR implementation.
5. Cross-walking Data Elements. Determine if a crosswalk is needed before you start the health data migration. They can become complex, so, think about how your use of LOINC, SNOMED, or RXNorm codes in the current system will parlay into the go-forward EHR. Does the new system use these codes as well? Or, if these codes were not used in the current system but are used in the go-forward EHR, what will the exercise be to match these up? This also is the time to consider the handling of provider codes, facility codes, pay codes, fee schedules, insurance providers, insurance plans, etc.
6. Securing Resources. If yours is like most organizations, often the IT staff is extremely busy building, planning for and training in the new system and you don’t have resources available to assist on items pertaining to the healthcare data migration. Three areas that require time and attention include: data validation in the test environment, manual clean-up on patient matching and building out the crosswalks. Don’t skimp in these areas. If internal resources aren’t available, make sure as trusted healthcare data migration vendor can provide these critical services.

Overcoming the Hurdles of Data Harmonization

If you consider all of the issues listed above, and multiply those by the number of different data sources you are trying to combine, you can see how converting to a new EHR can be a very difficult challenge. But, there is a way through the complexity. Seasoned resources who understand workflow can determine how and why your data was created, unlocking the value of how that data can be re-used to support quality improvement, population health, operational efficiency, or whatever business goals drive your institution. Harmony Healthcare IT and CETA solve these complex data challenges on a regular basis. If you need best practice recommendations or consultation to define a data strategy, please contact us.

Guest Blog submitted by Michelle Currie, RN, MSN, CPHIMS, CPHQ

Michelle is a registered nurse with more than 15 years of experience in Clinical Informatics. She received her BSN from the University of Michigan and her MSN in Clinical Informatics from the University of California at San Francisco. In addition to serving as Chief Data Officer at CETA, Michelle volunteers her time as an annual Conference Presentation Reviewer, Mentor and Moderator for HIMSS.

Editor’s Note: Some of the information contained in this blog has been updated from a previously published entry from 2015.

by Fria Kurowski, Account Manager and Privacy & Security Officer at Harmony Healthcare IT

If given the chance, I’m not so sure I would trade in my 25+ years of working with ERP software.  The challenge of implementing technology to manage such large volumes of critical information at a variety of organizations has been one I’ve thoroughly welcomed and enjoyed. While I’m still immersed in data day-to-day, I’m now working the flipside of the ERP life-cycle:  application retirement and HR record retention.  My most recent career focus is to help healthcare organizations develop a strategy for ERP data migration and archival so that systems like Lawson, Kronos, or API Healthcare can be decommissioned. While retiring ERP applications brings with it a number of strategic considerations and the occasional data hurdle, the ERP retirement benefits for the organization far outweigh the effort.

ERP System Replacement:  Conversion vs. Archival

I hear various reasons for why one ERP system is being replaced by another, but one question remains constant:  “what will we do with our legacy data?”  With years or even decades worth of critical workforce data in the breach, the development of an ERP data migration strategy is a must for healthcare organizations to comply with business record retention requirements.  One of the first decisions to be made is whether data should be converted into the new ERP or migrated off to an easily accessible archive.  As full system data conversion for volumes of human resource, supply chain, financial and enterprise performance management data are often too costly and complex, ERP data migration and archival has proven to deliver both a return on investment, user satisfaction for long-term access and HR record retention.

Considerations for ERP Data Migration to an Archive

When it comes right down to it, data is data.  So, whether we’re archiving vendor purchasing history/transactions, benefits history, W2 detail or time and attendance records from Lawson, Kronos or API Healthcare; the methodology is the same. Here are some considerations when approaching an ERP data migration project:

• Find an archive solution that will preserve immutable ERP data and scanned images in a secure environment for years to come
• Ensure that the data is easily accessible by users, categorized by topic, searchable by multiple data points (i.e., name, date of birth, social security number, employee ID) and capable of being sorted and filtered
• Consider converting only “master file” data to your new ERP software system. Inventory, Vendor, and Employee master files are typically straight-forward to import into your new system, saving you time and money.
• Ponder the possibility of using an archive solution for your historical transactions rather than attempting to convert them into the new software system. While most vendors would promise you how easy it is to convert transaction history, the reality is that each ERP system stores those differently and it’s typically not worth the time, money or aggravation to take them to your new ERP system.
• Eliminate the need for users to “remember” how to navigate an old Lawson, Kronos or API Healthcare system over the years for which records must be retained by making certain that multiple legacy data domains (i.e., HR, General Ledger, Accounts Payable, EHR, practice management) and multiple legacy data sources (i.e., Lawson, CPSI, McKesson, Mysis Tiger) can be consolidated into a single archive

The Benefits of ERP Application Retirement

As I’ve transitioned into the healthcare segment, I find that hospitals tend to just keep old software up and running!  With so many electronic health record issues and government mandates competing for attention from IT, system retirement seems to simmer on the back burner as a low priority.  My public service announcement to healthcare CIOs nationwide is this:  it doesn’t take that much effort!  If you partner with the right vendor, you’ll find that much of the heavy lifting for ERP data extraction, migration, validation and archival is handled.  The bottom line is that the benefits far outweigh the efforts, freeing up time and space for your IT team to optimize its EHR and focus on more pressing active data issues.

Fria Kurowski, Account Manager and Privacy & Security Officer

Fria is an Account Manager and Privacy & Security Officer for Harmony Healthcare IT. She has spent over 25 years working with Enterprise Resource Planning (ERP) software and is leveraging that expertise to work with hospital and health systems clients decommissioning ERP as well as other clinical and financial systems. She is responsible for the policies and procedures that protect the confidentiality, integrity, and availability of HHIT information systems and protected health information.

Editors Note: This blog contains content from an earlier blog post on September 4th, 2015.

Healthcare IT teams are faced with ongoing maintenance and other associated costs tied to retaining historical patient data long-term. Often, only critical data is migrated or converted to the go forward platform like Cerner EMR or Epic EMR. This leaves a challenge: what do you do with old systems being kept around due to medical record retention policies?

Harmony Healthcare IT VP of Program Management, Jim Hammer tackles this topic in his article published on LinkedIn.com. Read it here.

Need more information about health data archiving and converting to Epic EMR? Get Harmony Healthcare IT’s free Industry Brief “Legacy Health Data Management, an Overview of Data Archiving & System Decommissioning with Rick Adams”.