September 14, 2004
Chairman Hungate, members of the Workgroup on Quality, ladies and gentlemen, good morning. I am Dan Rode, vice president of policy and government relations for the American Health Information Management Association (AHIMA). Joining me this morning is Barbara Siegel, director of Health Information at Hackensack University Medical Center in northern New Jersey and immediate past president of AHIMA. On behalf of the Association and its members, thank you for allowing us this opportunity to provide input today on issues related to your workgroup's recommendations for Measuring Health Care Quality.
For those of you who are not familiar with AHIMA, we are a professional association representing more than 48,000 members who manage patient medical and clinical information in the form of health records and databases. HIM professionals work in provider, health plan, government, research, and other public and private organizations, facilities, practices, and agencies. The vital functions our members perform require them to intimately deal with a variety of issues that have become the subject matter for the NCVHS, both due to its original charge related to health data and statistics, and its more recent charge to advise the Secretary on the administrative simplification issues contained in the Health Insurance Portability and Accountability Act of 1996 (HIPAA).
As you will clearly hear in Barbara's testimony, AHIMA members, serving as directors of various health information management (medical record) departments for various healthcare providers and health plans, are usually charged with providing health information and data to a variety of third parties in the healthcare services cycle, including health plans or payers; state departments of health, public health, or welfare; state, employer or similar health plan quality initiatives, and so forth.
Unlike most others testifying today, AHIMA members work for the supplier of the data included in the recommendations under discussion. This means they not only supply the final product – data – in some format to the requestors, but also ensure the completeness and integrity of the data. To do this they must work closely physician and clinical staff, the producers of the data, and in ensure the data reported receives appropriate confidentiality and privacy protection.
For 77 years, AHIMA members have managed health information and records and, equally as important, the confidentiality of health information on a daily basis. Today, our members and the industry face an environment rapidly moving toward the era of a standardized electronic health record and a national health information infrastructure. But we are not there yet, and many of our members and their organizations in the health industry are facing this implementation at different levels of adoption. It is fitting, therefore, that this workgroup both address our industry's long-term goals for quality of care and patient safety at the same time it focuses on the process for collection and adoption of data and information to meet that end.
Before I provide some comments regarding the Workgroup on Quality's recommendations, I would like to reintroduce Barbara Siegel, who will describe the environment facing many of our healthcare industry's providers and, especially, hospitals. Barbara Siegel, MHA, RHIT
Good morning, Chairman Hungate, members of the Workgroup on Quality, ladies and gentlemen. My name is Barbara Siegel. Currently, I am the director of Health Information at Hackensack University Medical Center (HUMC), a 680-bed tertiary facility in northern New Jersey. Our facility is a participant in many data reporting initiatives including the Institute for Healthcare Improvement Pursuing Perfection grant, the Centers for Medicare and Medicaid Services (CMS) quality demonstration with the Premier Alliance, and the State of New Jersey. I speak to you today as a department director whose responsibilities include leadership over 175 employees as well as participation in organizational process improvement initiatives including revenue cycle, performance improvement, information technology, staff development, and public reporting.
As Mr. Rode indicated, I have been an active volunteer leader for AHIMA and the New York and New Jersey state associations, having most recently served on the board of directors of AHIMA for the past six years. In the course of my time in both national and state voluntary positions, I have had the wonderful opportunity to meet and talk with many HIM professionals from all different work settings. So today, I come to you not only with my own expertise and facility experience, but also with a good grasp of how the issue of data collection and reporting is affecting my colleagues across the nation. Health Information Managers and Quality Data Reporting
Health Information Departments, such as the one I lead at HUMC, are a significant participant in the public reporting process in most hospitals, health systems, and large clinics. Health information management (HIM) professional staff are the experts in abstracting data and coding.
We all recognize that perfectly delivered care does not equal perfectly documented care. Nor does it equal perfectly abstracted or coded care. Also population data is based on coding. Physicians and organization leaders want perfectly provided care reflected in the abstracting and coding. HIM professionals, through education, training, and experience are best positioned to recognize any disconnects between the rendering of care and the coding that reflects this care. In my reading of the workgroup's recommendations, I might add that HIM staff are also best positioned to connect data reporting requirements to the documentation provided by the medical staff. HIM professionals, having been educated and credentialed to provide accurate records, are keenly concerned when data requested cannot be reported using the classification systems and coding currently in place. This is one of the reasons we strongly support the workgroup's recommendation related to the adoption of ICD-10-CM. HIM professionals would like to use a classification system that truly reflects the medical care documented in the record, and a classification system that will provide much better data to meet the quest for superior information related to quality and patient safety. I would now like to address the functions of data collection and reporting.
At HUMC, with the increased data requirements for the CMS's 7 th Scope of Work (SOW) our organization leadership determined that advanced practice nurses (APNs) could no longer take time from patient care responsibilities to collect, collate, and report findings. In addition, the nuances of coding and abstraction were not fully understood as an integral component of data collection flow and analysis. As “neutral” participants – removed from direct patient care processes – HIM-credentialed staff therefore joined the disease-specific project teams. Their responsibilities include: data abstraction, data entry using vendor tools, error adjudication, reporting at monthly team meetings (including variance tracking, composite scores, unit specific scorecards), communicating revisions, or additions to indicator definitions.
I want to emphasize that HUMC is not only collecting and submitting data and waiting for it to be reported to the public, perhaps some six months to a year later. We are using the findings internally, daily, to improve processes that improve patient outcomes.
At HUMC, we have 6.0 FTEs (full-time equivalents), who serve as health information clinical data analysts (CDAs) and currently abstract 500 cases, on average, per month to meet the reporting requirements of the state of New Jersey, the JCAHO, and the CMS demonstration project. Our CDA staff includes: Three full-time registered nurses, one part-time physician, and three HIM professionals with RHIT and CCS credentials. [i] In addition to these staff, the inpatient-coding manager and I spend at least 30 percent of our time supporting staff needs and meeting with and educating our physicians – both hospital-based and residents. Multiple Quality Data Reporting Activities
Some of our data reporting projects are mandatory – those of the New Jersey Health Department and JCAHO for instance. Some , such as Leapfrog, are voluntary. Unfortunately, indicator definitions sometimes vary among initiatives and outcomes are not comparable. In addition, as each group increases the number of indicators to be abstracted, we must decide which voluntary projects to continue. Our CDAs work only on these quality initiatives. Their average salary is $32/hour. Until we have an electronic health record (EHR), the cost to the organization will remain high and will continue to increase as the demand for more data exceeds efficient methods to collect and report it. Obviously, the incomparability of the data requested directly impacts the cost of providing such data . I cannot overstate the need to achieve industry and consumer consensus and support for standardized data, data definitions, and reporting, as suggested in your May 2004 recommendations.
To illustrate these issues, I have attached two items to our testimony for your review. The first is a Public Reporting Project Cross-Walk, which will give you an idea of the various projects we have underway and the indicators involved. The second attachment is Clinical Data Management FTE Data Map, to illustrate the personnel involved in many of our other reporting projects.
There are 80 acute care hospitals in New Jersey. A total of 30 of these hospitals participated in Leapfrog's initial three leaps (computer physician order entry, evidence-based hospital referral, and ICU physician staffing) by completing a 20-page survey. Leapfrog has now added a fourth “leap” using 27 of the National Quality Forum patient safety indicators, which requires a 100-page survey with questions that apply to each of these 27 areas. Questions are asked on how implemented safety processes improve safety. For example there is a question on how adding an anticoagulation service impacts patient outcomes.
While the goal of these quality efforts is admirable, the number of participating hospitals has dropped down to ten – mainly due to man-hours, and the cost required to participate. This decrease takes us in the wrong direction and leaves New Jersey residents with only ten hospitals on the list to choose from. Employers want reliable data so that their employees and families make informed decisions based on quality outcomes, resulting in more value for dollars spent. New Jersey employers and their employees now have fewer hospitals to choose from because the institutions can't respond to all the data requests.
Data Collected Is Not Uniform
The healthcare organization's burden is not just a dollar figure, or an FTE count. Projects, outcomes, process measures, and data definitions are just not integrated in any meaningful way. For example, inpatient mortality may be defined differently according to the project sponsor organization. How do we explain to physicians and executive staff why our results vary depending on the study sponsor (for example, CMS v. JCAHO v. Healthgrades), or whether they are all payer-refined DRGs or other methodologies for detailing severity of illness? For example, when comparing outcomes for CMS's 7 th Scope to the statewide database for CABG – the results do not line up with other mortality risk data because the data definitions or the severity adjustment methods are different. Another example is in the CMS demonstration project, which uses the JCAHO method for AMI; the 3M APR-DRG for CABG/HIP and Knee; and the AHRQ model for postoperative safety indicators. Each model's reliability is not in question; it is when the models are combined that a problem occurs. Internally, our physicians discount the data, because they cannot understand why we have a good rate with CMS but not with the state database.
Multiple data definitions for the same indicators result in conflicting data. If we as healthcare professionals cannot understand it, how is the public able to make informed decisions? Data available to consumers should be understandable and uniform to allow for informed decision making in order to access care at places that deliver true deliver higher quality care.
For physicians and hospital leaders to believe publicly reported data is a legitimate tool, we must have evidence that the public looking at these Web sites can, in fact, make educated informed decisions. What does it mean to be a five-star versus a one-star hospital on the Healthgrades Website? Is Healthgrades able to tell us the number of hits that led to an informed decision?
The Clinical Data Abstraction Centers (CDAC) are validating data for the CMS's 7 th SOW (five cases per quarter) and the CMS demonstration project (10 cases per quarter). These reviews occur six to nine months after the close of a quarter. However, the data submitted to the Leapfrog project is not validated. For data to be valuable to the provider, the receiver, and the consumer, there must be consistency in definition and analysis. Hospitals and physicians cannot continue to hire FTEs to collect data without some documented benefit from this activity. Certainly the value will be proven when collected data is used to improve quality in the form of feedback to the providers, but it will also be proven when patients can go to a Web site or their family physician and obtain consistent, uniform proven data that can assist them in making their health care decisions.
The question for this workgroup is how will the healthcare industry and the employer-purchaser know that data is consistent, uniform and proven? How will we validate this? I believe the answer is, in part, in some of your recommendations, and in calling for consistent, uniform data to be agreed to by a national (quality standards or population health statistics) body, for use by all data collection groups. The Workforce Effort Must Be Efficient and Effective
As you all know we are facing a significant workforce shortage in healthcare. This shortage extends to a variety of nursing positions including nurses who now work in the area of utilization review and quality assurance. We also have a shortage of coders and other qualified health information managers. The inconsistencies of the requirements exacerbate the need for skilled professionals to perform our current quality data activities. In the meantime, moving more nurses into functions to work with coders in order to prove we are giving quality care takes away from nurses that can actually provide quality care. Another hospital network in the New York metropolitan area also uses four RNs, who travel between member hospitals to abstract data. Is this a valuable use of a resource in short supply?
Data collection is not new and has always been resource intensive. For years we have submitted data to Registries: birth certificate, death certificate, trauma, cancer, cardiac, and pediatric (NACHRI). More registries are on the horizon: diabetes, coronary artery disease, heart failure, COPD, and gastric bypass – all to identify centers of excellence for patients and payers.
We have seen longitudinal registries demonstrate that outpatient follow-up prevents inpatient exacerbations; thus proving to health plans that it is effective and prudent to cover such care. In the future we will have longitudinal EHRs to cover inpatient, outpatient and other ambulatory episodes of care. How do we take all this data supplied by healthcare providers daily and make use of it for population quality and public health? Is it time, now, as we build a standard EHR, for the healthcare industry and its consumers to determine just how we will gather data; resolve gaps in our current population information, and make such data collection, reporting and analysis, work efficiently and effectively for all parties concerned. Quality Measurement Requires Buy-In
For quality measurement to work, physicians must buy in to the process measures and indicators. However, not all measurements and indicators have physician agreement. For example: our physicians have a problem with organ transplant and bone marrow transplant patients receiving influenza and pneumonia vaccines while under high dose immunosuppressants. In such cases physicians believe such patients will not mount antibodies to the vaccine and the vaccine is a wasted resource. They believe that such patients should be vaccinated when their acute rejection episode is over. However, CMS does not agree. The community acquired pneumonia indicator for vaccine applies to patients over age 65 anytime during the year and the flu vaccine applies to all patients, October through March. If we comply with CMS instructions we must somehow force our physicians to order care that they disagree with, or we can support the position of our excellent transplant staff, and not follow the CMS directive, causing our “quality indicators” to fail.
Making a CMS care directive tie into our quality reporting, when our physicians have a reasonable disagreement not only puts our institution in a win-lose situation, it also causes our medical staff to question other CMS “quality” indicators as well. We decided that the reporting of public data in this case was secondary to quality patient care. So, we fail the CMS quality indicator and tarnish our reputation for quality care.
It is important, as the NCVHS and the Secretary guide the industry towards data standards for collection of population data, that providers of care be included in the establishment of such data standards. This would compliment not complicate the process.
In this short time I have tried to give you a picture of how data quality functions impact a large healthcare facility. My colleagues and their organizations across the country face similar challenges that vary depending on their size and the data reporting requirements in their localities.
The collection of data for healthcare, whether it be for delivery of care or for development of evidence-based protocols, or other important healthcare objectives, is a key reason behind the recent efforts for the development of a health information infrastructure. The current development of a standard EHR provides a mechanism to collect and store data, within our organizations, in a uniform manner. All this effort, however, will not bear fruit, if the segments of our industry interested in such data cannot come together with federal leadership to resolve the issues I have discussed. The time to achieve consensus is now, and I wish the workgroup and the NCVHS much success in pushing your agenda forward. My colleagues at HUMC, and my fellow professionals in New Jersey and across the nation look forward to your resolution and leadership in this very important aspect of health information management.
Mr. Rode will provide our concluding statement. Dan Rode, MBA, FHFMA
As Ms. Siegel noted, we commend the Workgroup on Quality for moving forward on the May recommendations and for making the effort to identify key values and data that can assist our industry in improving the quality of care and patient safety, within our healthcare organizations, and contribute to increased quality of care, public health, and other population health improvements.
The NCVHS's involvement in data standards is key to resolving the barriers to the efficient and effective collection of health information. AHIMA believes the collection, storage, analysis, and dissemination of accurate healthcare data serves a variety of critical purposes under the umbrella of population health. Healthcare data must not only serve to improve care within the individual organization, but also throughout the community and across the nation. This healthcare data and the uniform process(es) that can be used to transmit such data serve to improve public health, the reimbursement process, healthcare policymaking and healthcare research as well as those identified as “quality.” Together these functions can lead to population health improvement – a primary goal of the NCVHS and the Secretary. Standards for Population Data – A Consensus Process
To make these improvements we must have standards. Ms. Siegel's testimony illustrated the conflicts facing our physicians and clinical staff when the data desired does not match medical practice. She also shared the conflicts our HIM professional members face when the data, terms, definitions, values, and measures do not match. Standards must not only provide for a consistent and efficient method for collection, analysis, and dissemination of healthcare information, but must also ensure that data is comparable and consistent when viewed by consumers, private and government bodies and, various public health agencies.
Previous testimony to this workgroup pointed out that there is tension between those who want to complete the quality task now and those who want to build a standard electronic health record (EHR) and an infrastructure that will serve multiple purposes, including quality improvement, in the future. We believe the NCVHS can again lead the way by providing the industry and the Department of Health and Human Services with a recommendation that will allow a public-private collaboration for the collection of uniform, standard, population data as we build the standard EHR and infrastructure, while also addressing our short term quality needs.
In making this last statement let us be sure that everyone recognizes that data collection requests come not only for improvements in quality, but also for public health, bioterrorism, patient safety, and a variety of oversight and other public reporting purposes. The standardization of data and data definitions must be uniform across for all these uses, just as the data in our HIPAA [ii] transactions is meant to be standard across all HIPAA entities. We hope that as a consensus process is developed you will also consider adding all of the various registries that now exist or are in development. Involving these registries is needed to improve the data collection process, and because their aggregated data is valuable for greater healthcare improvement. Any recommendations toward a consensus process should also provide for involvement of clinicians and HIM professionals from across the healthcare spectrum, in order to ensure acceptable and workable standards.
Integration with the Standard EHR
Agreeing to standardization of data, data definitions, and data sets is one step. Any standards setting body or group must also be charged with the ongoing task of ensuring that such data keeps pace with the changes in medicine and the public and private needs for information. Such a body must also be actively engaged with the current efforts to establish a fully functioning, standard EHR. Ensuring that the standard EHR contains the information necessary to report data from healthcare providers will greatly increase the efficiency needed. Now is the time to move forward with such a recommendation as the Office of the Coordinator of Health Information Technology (OCHIT), the Agency for Healthcare Research and Quality (AHRQ), the Health Resources and Services Administration (HRSA), and a variety of private bodies are all moving in this direction. Specific Questions
Throughout our remarks I believe we have addressed several of the questions generated by staff for today's hearing, but let me just respond to a few specifically:
First, let me note that AHIMA would be opposed to the inclusion of further data associated with population health improvement on the healthcare claim form. For the purpose of maintaining an individual's health information privacy and confidentiality, we do not believe it is appropriate to report such data with the claim and pass it throughout the health claim adjudication processes.
We are aware that many locations use the UB-92 and other forms to report healthcare data separately from the claims process. While we are not against this practice, we believe it would be better to develop a separate transaction for the purpose of sending such information. Any such transaction should have the same data definitions and data sets as those developed for the standard EHR so that healthcare providers and their systems vendors might develop more efficient reporting mechanisms.
Second, as Ms. Siegel indicated, many organizations find the internal collection, analysis, and dissemination of quality information to be beneficial. However, to achieve buy-in to the external reporting of data, there must be professional involvement and agreement on the data to be collected and the standards for the reporting of such information.
Third, while Ms. Siegel provided you a personnel cost for such data collection at her institution, we do not have data to indicate the full cost of such collection across the country. The data reporting burden increases as diversity, rather than standardization, takes hold. Obviously, those who must rely on a paper-based record system or a hybrid system (common to many hospitals today) experience a much higher cost than those who can rely on a electronic medical record. A standard EHR and data standards would significantly reduce the cost of data acquisition, analysis, and dissemination.
Fourth, while we do not believe it is appropriate to comment today on the concept of pay-for- performance, we do believe it is important to note that unless we have standards and buy-in from those producing the original documentation from where data is abstracted, there will be considerable dispute as to the equity of such pay-for-performance processes. Likewise, introduction of a pay-for-performance process, based on a standard EHR, and data definitions, should be preceded by some effort to ensure all healthcare entities can economically purchase the software and hardware necessary to meet the reporting requirements.
Fifth, the candidate recommendations should be considered as a group in line with our other comments and recommendations. While they might serve as the first or core group of data elements for consensus and standards, we recognize that there will be additional recommendations from other groups as well. We have reviewed these recommendations with various HIM professionals, and recognized that such data is not consistently defined or sought in various parts of the country.
Unfortunately, the workgroup's questions relating to the collection and storage of race and ethnicity data arrived too late for us to perform any assessment of our member's processes in this regard. I also believe obtaining a good picture of how such collection is performed to the detail level of your questions will be quite a challenge, since this is not a function that has been standardized to any extent. Companion Recommendations
While the workgroup has requested comments on its first eight recommendations, our comments have also been directed to Recommendation 10, which relates to the standardization of currently inconsistent data items. We obviously strongly support this recommendation and hope that you will move forward on it as soon as possible. Clinical Terminology and ICD-10-CM
We would be extremely remiss if we did not comment on two other recommendations of the workgroup, namely: Recommendation 14 – to adopt standard clinical terminologies, including a crosswalk or metathesaurus of clinical synonyms, and Recommendation 16 – to adopt ICD-10-CM for coding of classification of diagnoses and health conditions. To some extent the NCVHS has moved forward with these recommendation, but today, much work still needs to be done and the Workgroup on Quality and the NCVHS recommitment to these recommendations is very important. The recommendation to adopt ICD-10-CM has gone unanswered to for over nine months. We cannot understand how some organizations within the healthcare industry that desire further detail regarding diagnosis and similar data, continue to insist on using a 30-year old classification system (ICD-9-CM), when the federal government has developed an updated and improved classification system (ICD-10-CM) that can produce significant detail, including, in some instances, the identification of severity levels.
The Secretary's previous announcements regarding SNOMED-CT® greatly support the workgroup's recommendations for clinical terminologies. Adoption of ICD-10-CM could speed up the crosswalk and mapping recommendations of the workgroup. We believe that a comprehensive recommendation is needed to signal the government's desire to see SNOMED-CT® as the basis for a standard EHR, and for ICD-10-CM to be one of the classification systems used for the reporting of data from the EHR. This is important to get the industry moving forward now, rather than waiting another decade. Mappings and crosswalks are needed, and there are government and private resources are available to undertake these projects; but today, as we meet, this process is at a stand still. We hope the NCVHS will join with AHIMA and other healthcare groups and get the ball rolling on this again.
The year 2004 has been a significant milestone in our progress to achieve a standard EHR and the infrastructure to exchange data for a variety of purposes that will improve population health. The collection of data for quality improvement, public health, and other purposes that make up population health improvement, will be greatly accelerated and improved through standardization and inclusion in the EHR and infrastructure. The workgroup and the NCVHS are at a point where much can be done to move this process along. We again congratulate the Workgroup on Quality on your championing these candidate recommendations and extend our expertise and resources to help you move this process forward wherever possible.
Ms. Siegel and I will be happy to answer any questions the workgroup might have. Thank you.
|Dan Rode, MBA, FHFMA |
Vice President, Policy and Government Relations
1730 M Street, NW, Suite 409
Washington, DC 20036
Telephone: (202) 659-9440
|Barbara Siegel, MS, RHIT |
Director, Health Information
Hackensack University Medical Center
Hackensack, NJ 06071
Telephone: (201) 996-2072
[i] RHIT stands for Registered Health Information Technologist. CCS stands for Certified Coding Specialist. Both certifications are offered by the American Health Information Management Association (AHIMA) (http://www.ahima.org/certification/) .
[ii] Health Insurance Portability and Accountability Act of 1996.