Galen Healthcare Solutions: Allscripts Consultants Enterprise EHR

CMS released a couple of updates last month regarding Meaningful Use and the EHR incentive program. I wanted to pass this information along to our readers.

In their December 7 update, CMS indicated that “HHS announced its intention to delay the start of Stage 2 meaningful use  for the Medicare and Medicaid EHR Incentive Programs for a period of one year for those first attesting to meaningful use in 2011”.  The reason as such, according to them, is that the current schedule for compliance to Stage 2 could be a challenge for those that attested in 2011. The decision also was in consideration for vendors and practices.

 The CMS update identified some benefits from the proposal:

• The delay could provide vendors more time to develop their certified technologies for Stage 2
• The delay could also provide providers more time to implement the new software to meet Stage 2 requirements
• Expectations remain current so that providers attesting in either 2011 or 2012 begin Stage 2 in 2014
• And while 2011 has passed, CMS believed this idea would provide added incentive for providers to attest in 2011.

While I am sure there is a group of people out there that is ambitious enough to keep pace for this process, I am certain that we all can stand to benefit from the proposed delay.  The benefits from the added amount of time for both the vendors and practices/providers seem more appealing, in my opinion.

Back on December 1, CMS also announced a new tool to help Eligible Professionals (EPs) through the phases of Meaningful Use.  This tool is an eighty-five (85) page PDF file, dubbed as a “Beginner’s Guide”. This file provides a thorough, interactive walkthrough of Meaningful Use.

Among the items of information provided are:

• EHR Incentive Program basics
• How to participate (determining eligibility and registration)
• Meaningful use and choosing measures
• Attestation
• Helpful resources on the Medicare and Medicaid EHR Incentive Programs

Lastly, they also provided a link to their Educational Materials page for the EHR Incentive Program. This link offers an extensive array of files and tools regarding the EHR Incentive Program.  This is definitely a link to bookmark, as well as the guide previously mentioned.

If you haven’t already done so, visit the CMS EHR Incentive Programs webpage and register to receive their email notifications. 

Contact Galen Healthcare Solutions for any additional questions regarding Meaningful Use and Allscripts Enterprise^TM EHR.

Tags: Allscripts, Allscripts Enterprise EHR, EHR Certification, Enterprise EHR, Meaningful Use

This past Tuesday, I attended the NEHMISS Monthly Event and Social hosted at the Papa Razzi in Wellesley, MA with one of my colleagues, Patrick Zummo. It provided an invaluable opportunity to network with other healthcare IT professionals as the event had one of its best turnouts in the past two and a half years that we have attended (I would estimate about 65 attendees!) It was great catching up with folks and seeing new faces. The networking opportunity can’t be underscored enough!

The event featured a presentation on “An Approach to Meaningful Use” by Laura Leinin, Sr. Project Specialist, Clinical Information Systems at Partners Healthcare, and Jennings Aske, J.D., CISSP, CIPP, Chief Information Security Office at Partners Healthcare.

Laura started things off with an overview of the MU legislation to date:

• Original goal of MU legislation was to target full EHR adoption by 2014
• The three phase of EHR: Adoption – MU – Outcomes
• Compuer Sciences Corporation (CSC) estimated that for a hospital with 276 beds, the potential MU payout would be $4.2 million
• The major certifying entities for EHR software is Certified Commmunication for Health Information Technology (CCHIT) and the Drummond Group. The model utilized is a conformance tool where submission of data is required.
• Attestation if currently a manual process – submission of the numerator and denominator.

Jennings followed by addressing security compliance & MU:

• The main components of security compliance include access control, emergency access (“break-glass” capability), auto log-off, and audit log.
• Jennings noted that with the audit report, requirements included capture of userid, patientid, user activity and the ability to sort by time.
• An example of compliance presented was since Partners had a home-grown EHR system (longitudinal medical record – LMR), in order to comply with CCHIT & Drummond certification, the system needed to possess the capability to handle encrypted file import.
• Jennings expressed that Partners needed to exhibit compliance as described above, but in some scenarios (like the encrypted file import described above) they don’t actually intend on using the functionality. This led me to wonder what the percentage of cases were where the EHR needed to comply with security standards, but would never actually use or implement the feature in operational practice.

For the remainder of the presentation, Laura offered some statistics and updates with regard to Partners MU initiative:

• As of Thursday, September 30^th, 114,644 EPs & EHs have registered for attestation.
• As previously noted, attestation for stage 1 is currently a manual process and Laura warned of the high chance that organizations are likely to be audited post-attestation and as such they should have the records and data to back it up.
• She noted that Academic Medical Centers (AMCs) need to be self-certified in that they often have home-grown systems in the inpatient setting and noted that community hospitals often have commercial off the shelf (COTS) systems provided by the leading EMR vendors.
• She provides a project status dashboard each month to stakeholders and executives with more than 125 data points to track each month!
• Laura also mentioned the challenges of qualifying for MU in the presence of the healthcare information system mosaic at Partners that we’ve previously touched on in our blog in that of the different organizations that are affiliated with Partners (Brigham and Women’s Hospital, Massachusetts General Hospital), there are different vendor systems for ED (Electronic Discharge) systems, PM (Practice Management) systems, etc.
• In some cases, to qualify for meaningful use, workflows had to be adapted. One example was the handout of clinical summaries to patients.

Several great questions were posed by the audience including the following

• Q: Is standardization of vocabularies at Partners being handled by IT or the clinical staff? A: IT staff
• Q: What’s the headcount needed at Partners for the MU initiative? A: About 50 people across hospitals and LMR teams
• Q: How is Partners handling the case where smoking status is not captured discretely, but rather exists in a note? A: The homegrown LMR at Partners currently captures smoking status discretely. However, there are NLP (Natural Language Processing) solutions (Autonomy, Nuance come to mind) to post-process the non-discrete data for those applications which do not store it discretely. We have touched on data-mining non-discrete data in a previous blog post.
• Q: What happens if an organization decides to switch an EHR going forward? How is certification and MU qualification handled? A: No presenter or audience member had experience in switching organizations, but as we’ve witnessed with EHR vendor consolidation and an explosion of acquisitions requiring data conversions, this is likely to be a hot topic going forward.

Tags: Allscripts, Allscripts Data Mining, CCHIT Certification, Custom Reports, Drummond Group Certification, HIMSS Meaningful Use, Longitudinal Medical Record, Meaningful Use, Meaningful Use Security Compliance, NEHIMSS, New England HIMSS, Partners Healthcare Meaninful Use

Analytics Analysis, also known as crosstabs, is a tool for sorting data. It allows users to drill down into the data using fields available from the database and is very flexible. It is often used for viewing data at multiple levels, comparing data across sites or providers, and providing detailed worksheets about the data.

For more Galen webcasts visit: http://www.galenhealthcare.com/calendar/

Tags: Allscripts, Allscripts Analytics, Allscripts Crosstab Reporting, Analytics, Business Intelligence

The configuration and workflows relating to the Core Meaningful Use objective Record Smoking Status can get confusing; and recent information was released indicating a change in some of the setup for this measure.  Record Smoking Status requires that providers report that more than 50% of all unique patients 13 years-old or older seen by the eligible professional (EP) have “smoking status” recorded as structured data.  CMS has altered its reporting requirements for this measure so that now reports should only include the CDC smoking statuses.  Those smoking statuses include:

• Current Every Day Smoker
• Current Some Day Smoker
• Former Smoker
• Never Smoker
• Smoker, Current Status Unknown
• Unknown if Ever Smoked

You may be asking yourself, what do I do because providers at my practices have been entering terms other than the ones above as the patient’s smoking status?  Allscripts has developed a script that links previous smoking terms to the terms required to meet the measure in order for the provider to get credit on all smoking terms documented.  One thing to keep in mind when running the script is that it does not always match the terms on the patient’s chart to the most accurate CDC term.  For instance, a denial of smoking documented on the patients chart could fall under two separate CDC terms, “Former Smoker” and “Never Smoker”, but since the script can only link to one term, it chooses “Unknown if Ever Smoked”.  “Unknown if Ever Smoked” is not the most accurate, but the provider does get credit for the Meaningful Use measure. 

Because the script does not give the most accurate information for reporting, it is recommended that providers discontinue using the “denied” option when documenting smoking statuses and add the CDC smoking diagnoses to their quick list for easy reference for physicians.  Providers should attempt to use the CDC smoking terms to identify a patient’s smoking status as often as possible.

The CDC smoking diagnoses can be added to provider’s quick lists using SSMT.  The Content Categories of Favorites: Patient Hx – Active Problem or Favorites: Patient Hx – Social History can be utilized to identify the quick list items.  The steps are as follows:

1. Manually add all of the CDC smoking diagnoses to a user’s social or active problems list. (depending in which problem section the providers will be documenting the smoking status)
2. Extract for that user the Favorites: Patient Hx – Active Problem or Favorites: Patient Hx – Social History content categories from SSMT.
3. Open an Excel spreadsheet.
4. Highlight the whole spreadsheet, right click, and choose Format Cells.
5. Choose the Category of Text and click OK.
6. In SSMT, use CTRL+A to highlight all the text and CTRL+C to copy the text.
7. Paste the text in the Excel spreadsheet.
8. Copy columns B through J and paste on a new Excel spreadsheet. With the same formatted cell settings.
9. In column A, type the username of the provider you want to add the favorites to.
10. Make sure a Y is in the column labeled TopFavoriteFlag and in the column labeled Create.
11. Repeat steps 8 through 10for all providers that need the smoking statuses added to their Quicklist.
12. Copy all fields and paste them into SSMT.
13. Click the Import button.
14. Confirm the import worked for a few users.
15. Repeat these steps if setting the quick list in both Active and Social History problems.

It is important to note that the CDC smoking terms were delivered in Q3 and Q4 2010 Medcin releases.  It is required that these releases be installed in Enterprise EHR in order to meet the Record Smoking Status Meaningful Use objective.  In Allscripts Enterprise EHR^TM version 11.2 HF 9, the Record Smoking MU Alert will be linked to the CDC smoking terms and the provider will be able to reconcile this alert by selecting the appropriate term from the list of smoking terms.

An additional recommendation for configuration includes setting the TWAdmin preference Smoking Status for Patients 13 and Older is Not Documented to “Show in My Alerts”.

The configuration and workflows for the Core Clinical Quality Measure Preventative Care and Screening Measure Pair: Tobacco Use Assessment and Tobacco Cessation Intervention is often confused with the configuration and workflows for the Meaningful Use measure Record Smoking Status.  Although the terms used for recording the smoking status are applicable for recording tobacco use, additional workflow is required to meet the Quality Measure.  The Quality Measures can be reviewed in more detail in the Quality Measures PDF on the Allscripts Client Connect website.

Tags: Allscripts Enterprise EHR, Meaningful Use

This morning, Shahid Shah over at the The Healthcare IT Guy blog, published an article outlining why medical device data is the best way to fill meaningful use EHRs and conduct comparative effectiveness research (CER). What was of particular interest to me is the way in which Shahid elegantly broke down how unstructured and structured data is “sourced” today (scroll down in the blog article for the graphic).

As is evident by the table above, many of the existing MU incentives in Phase 1 (patient reported and healthcare professional entered especially) promote the wrong kinds of collection: unreliable, slow, and error prone. Accurate, real-time, data is only available from connected medical devices and labs / diagnostics equipment.

Given that meaningful Use and CER advocates are promoting (structured) data collection for reduction of medical errors, analysis of treatments and procedures, and research for new methods it’s important to see that we’re not going to get real gains until the medical device vendors are fully connected and providing data directly into EHRs or clinical data warehouses.

Shahid’s article brings to light a larger issue within the industry – a lot of meaningful data is captured in an unstructured fashion. Dr. John Halamka brought this to light in a blog article earlier in the year which addressed “Freeing the data.” In this article, Dr. Halamka suggests that businesses will always have a combination of structured and unstructured data and that businesses must find ways to leverage this unstructured data:

In healthcare, the HITECH Act/Meaningful Use requires that clinicians document the smoking status of 50% of their patients.   In the past, many EHRs did not have structured data elements to support this activity.    Today’s certified EHRs provided structured vocabularies and specific pulldowns/checkboxes for data entry, but what do we do about past data?   Ideally, we’d use natural language processing, probability, and search to examine unstructured text in the patient record and figure out smoking status including the context of the word smoking such as “former”, “active”, “heavy”, “never” etc.

The value of unstructured patient narratives was addressed in detail in one of last year’s Health Management Technology articles – specifically the section which addressed Mining unstructured data:

As EHRs become increasingly widespread due to the billions of dollars in federal stimulus incentives, harnessing unstructured clinicians’ notes gives us the power to yield valuable patient data. With each year of data, more information will be gathered that could be used to find predictors for diseases or adverse effects of treatment that would otherwise have gone unnoticed by most traditional research studies. Though challenging, capturing and delving into this data will be worth the effort, and could potentially help healthcare institutions meet requirements for CMS reporting and for meaningful use, access funding and, most importantly, improve the health of entire populations.

At Galen, we have developed a solution that addresses current limitations with regards to extraction of structured note data within built-in Allscripts Enterprise EHR functionality. Galen’s NoteXML solution is designed to facilitate the querying of data contained within Allscripts Enterprise EHR v11 Structured Notes. These notes are not stored inside the EEHR as discrete data, but rather as XML documents that aren’t easily query-able. The solution has helped our clients extract pertinent MU reportable data that otherwise would not be discretely available.

Again, the aforementioned solution does not facilitate data mining of unstructured note data. However, companies such as Nuance are engaged in “‘unlocking’ unstructured clinical documentation, sometimes referred to as the ‘narrative blob’” Nuance’s NLP solutions assist in collecting and reporting on various diagnostic, quality and safety measures. I have yet to see this integrate directly to the Allscripts product line, but anticipate this possibility in the future months.

I’m curious as to how other groups and organizations are addressing the gap between unstructured data capture and discrete data extraction for MU and quality reporting? Are organizations relying on third-party solutions such as that offered by Nuance?

Tags: Data Mining, EHR, EHR Unstructured Data Mining, Extensibility, Meaningful Use, MU, Natural Language Processing, NLP, Reporting, UNstructure EHR Data

Our clients environments are both sophisticated and complex, often times with different vendors in the fold for the different healthcare information systems that are utilized by the organizations. For those clients that are Managed Service Organizations (MSOs) or have different sub-entities, this is even more pronounced. Consider for a moment a scenario where an Integrated Delivery Network (IDN) consists of four physician groups under its umbrella. Some of these physician groups were added via acquisition – and as such were using existing systems such as EHRs or PMs from vendors different than those of the organization they were joining. The following mosaic illustrates such a case:

Given the graphic above, one can appreciate the complexity involved with the following core enterprise organizational functions:

• Interoperability – Most systems do not easily interoperate with one another and thus require interfaces to be developed to facilitate communication between the systems
• Patient Matching – uniquely identifying a patient across the enterprise in a system-agnostic fashion.
• Reporting and Analytics – Each of the systems may have different database technologies at their core, and additionally the structure of the data is sure to be different.  This creates a challenge in reporting metrics to exhibit adherence to meaningful use criterion for instance or to
• Trust – Which patient data should be shared across which systems?

A recent presentation at a NEHIMSS last month illustrated these points above and did a great job of communicating how Partners Healthcare is addressing the Healthcare Information System (HIS) mosaic via their COMPASS project. The COMPASS project is an aggressive initiative which implements a common administrative system and processes to streamline revenue cycle management and help manage costs through a “holistic, patient-centric, workflow-driven approach.”

The efficiency of the mosaic of systems (ala Claude Shannon for those EE nerds out there) is subpar at best. But this is the environment organizations find themselves. The alternative would be to consolidate to utilize one vendor across all systems ala the COMPASS project. However, some vendor systems are better at functions than others and the cost for conversion may be prohibitive or in some instances not feasible. For those organizations seeking out advice or recommendations for healthcare information systems, check out the folks at Software Advice as they offer great resources.

Contact us today if your organization seeks assistance with data conversion or integration of healthcare information systems.

Tags: Data Conversion, IDN, Integration, Interfaces, Interoperability, Meaningful Use, MSO, Multi-Org, Reporting

Integrating other web applications into the EHR is a dream for many.

Here, we take the risk calculator built based on the Framingham Heart Study, and integrate it into the Allscripts Enterprise EHR.  With the integration from Galen, the EHR sends the required fields into the Framingham Risk Calculator, like patient age, blood pressure and cholesterol, reducing the data entry and number of clicks for the clinician.  

Now’s it’s as simple to calculate the patient’s risk of heart attack, as it is to view their chart, all while in the EHR.

As we mention in the video, the Framingham Risk Calculator is just one example of integrating a website or web application into the EHR.

Galen has worked with groups on many types of EHR to web application integration – what would you add to the EHR, if you could?

Tags: Extensibility, Interfaces, Interoperability, Meaningful Use

One of the “Menu Set” CMS Final Rule Meaningful Use Stage 1 objective and measures specifies that “at least 40% of all clinical lab tests ordered whose results are in a positive/negative or numerical format are incorporated in certified EHR technology as structured data.” Additionally, the Certification Commission for Health Information Technology (CCHIT  - certifying body for EHRs) indicates via IO-AM 07.02 that “The system shall provide the ability to receive and store microbiology laboratory results with organisms recorded as free-text. (Not MU).” This brings to question the handling of interfacing microbiology results into the EHR.

Microbiology results are often longer, textual results including sensitivities. Additionally, microbiology results can have 3 levels of hierarchy:

1. Orderable item(s) (Urine Culture)
2. Culture(s)/Organism(s) (Light Growth Escherichia Coli)
3. Susceptibility(ies) (Amplicillin)

The problem is that most EHRs are not well-suited to rendering interfaced results with three-levels of hierarchy; rather, the EHR is suited for just two levels of hierarchy:

1. Orderable(s)
2. Resultable(s)

When the interfaced result is sent by the vendor as a “discrete” result, the result likely will not render in the EHR properly:

To accommodate for this, most vendors have the capability of sending the interfaced result as “non-discrete,” or in other words, sending a free-text version of the result.  However in an instance where the vendor is able to send “discrete” microbiology results only, the interface analyst is charge with developing a interface customization to translate the “discrete” result to file into the EHR as a “non-discrete”:

The disadvantages of filing the result as “non-discrete” include the likely lack of ability to aggregate or report on these types of results.

For reference the original printed report from the Laboratory Information System (LIS) for the example above (recall that if an interface is not setup, this is the report that is usually provided by the LIS via fax).

Please contact sales@galenhealthcare.com if you or your organization would like assistance in interfacing discrete/non-discrete results to your EHR.

Tags: Allscripts Enterprise EHR, CCHIT, Certification Commission for Health Information Technology, ConnectR, Database, Enterprise Order, HL7, Integration, Interfaces, Interoperability, Laboratory Information System, LIS, Meaningful Use, Microbiology Results

Issue:

A recent issue came up with one of our clients in that interfaced patient appointments from their Practice Management system were not making it in a timely manner to the EHR. The client witnessed that appointment messages built up in the interface queue and there was a delay in processing the messages. The client desired a resolution that would assist in speed up of the processing of the messages such that appointments booked in PM would render in the EHR quickly without a disruption to workflow.

Investigation:

Enter the ConnectR Toolbelt “Transaction Processing Time” report:

This report extracts transaction count, minimum, average, and maximum ConnectR processing time per hour. Using the report, the following analysis was conducted.

Findings:

Based on the aforementioned analysis, it was determined that in the clients Live Reg/Sched system target, blocked messages were being logged. Having blocked messages logged can be invaluable when first designing and developing interfaces. However, as evidenced in the analysis, it can lead to performance degradation as the system requires much less processing time when messages are not logged.

Outcome:

Logging of blocked messages in the Live Reg/Sched target was disabled on 6/30/2010 and as witnessed in the analysis spreadsheet the number of transactions decreased by roughly 70% and peak transaction processing time decreased by roughly 90%.

Tags: Allscripts Enterprise EHR, Business Intelligence, ConnectR, ConnectR Toolbelt, Integration, Interfaces, Meaningful Use, SQL Server Reporting Services

Galen Healthcare Solutions is proud to announce that we will be continuing our popular series of free webcasts this fall related to Allscripts Enterprise EHR.   These Webcasts will cover topics including Analytics, Allscripts Enterprise EHR Note, Interfaces, Reports, Allscripts Enterprise EHR Orders, Tech System maintenance.

Learn more »

Tags: Allscripts, Enterprise Order, Galen Webcast Series, Integration, Interfaces, Meaningful Use, Technical