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Module 11 Final Exam – Case Study

Module 11 Final Exam – Case Study.

I’m stuck on a Health & Medical question and need an explanation.

Performance measure data reports for the outpatient registration unit shows that the registration process contains some inefficiency which results in some patients being late for their appointments. Additionally an increased number of errors in patient information were found, which are time-consuming to correct and can delay billing for services.

As a member of the Performance Improvement (PI) team, you will work with the unit to find the causes of the process issues and then determine what actions will help to streamline it, saving time and reducing data entry errors. Your PI team will use the Plan-Do-Check-Act (PDCA) method.

In a 1-2 page paper:

  1. Briefly describe the tools and techniques you would recommend to be used at each phase of the PDCA process:
    • Plan – Proposals or ideas for addressing the process issues
    • Do – Implementing one or more of the proposed ideas
    • Check – Evaluate the effectiveness of each idea implemented
    • Act – Choosing the idea that helped meet the goal (improved process)
  2. Recommend an appropriate action for each step of the PDCA.

The process for collecting and reporting quality measure data can be challenging for healthcare organizations. Currently a number of associated tasks are still performed manually, including the retrospective review of medical records and administrative data. It also takes time to organize, analyze and then communicate the data to internal and external stakeholders. Furthermore, there are increased demands for quality measure data to meet regulatory and accreditation standards, to negotiate contracts with insurance companies and to qualify for incentive payments in value-based purchasing programs.

The increased emphasis on quality measurement and performance improvement data means that organizations must be able to generate and gather the data elements as quickly and accurately as possible. However these data elements need to be relevant to clinical and management staff so that quality goals can be set and business decisions can be made. The data also needs to be clearly defined and standardized so that they are interpreted and used the same way throughout the organization.

Health information technology is now being utilized more extensively to support quality measurement and reporting, especially through the electronic medical record (EMR) or electronic health record (EHR). Both EMR and EHR systems now feature capabilities that enable clinicians and staff to abstract quality data elements from patient records and generate reports in clinics and physician offices. There are acute care facilities or health systems that use EMR systems with clinical pathways, evidence-based research, or guidelines embedded in them to support clinical decision-making and compliance with established quality measures while treating patients, rather than after they have been discharged. Reports are then generated that show how often specific providers comply with quality measures, or the outcomes associated with following certain guidelines. There may even be edits to prompt clinicians or staff to take a specific action based on input into the system, which also contributes to improved compliance with quality measures.

Although the capabilities for more automated quality data reporting exist, not all healthcare facilities have access to systems to automate their reporting process. Electronic quality data reporting itself is still in development as the data elements for quality measures need to be converted from a manual format to an electronic format. There is also the issue of the data itself being fundamentally different, especially if data is captured during health care process or at the point of care rather than retrospectively. In the paper-based specification reviewers can interpret the concepts represented by the data elements, but the electronic patient record system must have a more “black and white,” standardized definition that encompasses the concept being represented.

The status of healthcare quality monitoring and reporting will change greatly in the near future, as it transitions from a labor-intensive administrative process to a more one that is more efficient and makes data available in “real time.”

eMeasures and Meaningful Use

The United States government established the Office of the National Coordinator for Health Information Technology (ONCHIT) to oversee the development of policy related to health information technology that supports improvements in health care delivery and outcomes, population health and health information exchange, especially for participants and beneficiaries in the Medicare and Medicaid programs. The ONC, as it is commonly known and CMS developed criteria to certify health information technology products, known as Meaningful Use (MU).

The purpose of meaningful use criteria is to ensure that health information technology contributes to better health care through better information exchange and to enable the submission of quality data in an electronic format. The Certification Commission for Health Information Technology (CCHIT) is the organization that evaluates health information technology for compliance with meaningful use criteria on behalf of CMS.

Hospitals and eligible providers (physicians) that participate in the Medicare and Medicaid programs were invited to participate in incentive programs to adopt health information technology that met meaningful use criteria. If the organizations could demonstrate MU, they qualified for incentive payments that would help them implement an EHR or EMR system.

Stage I

Stage I criteria was implemented in 2010 and required specific clinical quality measures (CQMs) than EHR system had to be able to report. Data was not submitted to CMS, but the providers had to attest to the fact their systems could generate the measures. These CQMs were similar to those that hospitals and eligible providers (physicians) reported to CMS in a paper-based format, but they were redesigned so that the data elements would be captured by the EHR system and generated as eMeasure data. The specifications for eMeasures use health information technology standards such as quality data reporting architecture (QRDA) to build a measure in the EHR. The data elements themselves are based on classification systems like ICD-9-CM or ICD-10, or clinical terminologies like SNOMED (clinical concepts) or LOINC (for laboratory messaging).

The eMeasures selected for MU were chosen because they cover the six domains for quality health care identified by the Department of Health and Human Services (DHHS):

  1. Patient and Family Engagement
  2. Patient Safety
  3. Care Coordination
  4. Population/Public Health
  5. Efficient Use of Healthcare Resources
  6. Clinical Process/Effectiveness’

Stage II

The eMeasures for Stage II MU criteria will be collected during 2014. Hospitals and eligible providers can choose from recommended measures for adult or pediatric patient populations.

Stage III

Stage III MU eMeasures will be announced in 2015.

The public reporting of electronic quality measure reporting began in early 2014, as CMS is utilizing a clinical data registry for reporting quality measures used for the Physician Quality Reporting System (PQRS). The National Quality Forum along with the Joint Commission and other quality measure development organizations currently working on pilot projects to collect eMeasure data from hospitals for analysis and comparison with current paper-based specifications. There will be differences in the results of quality measure data captured manually vs. electronically, because eMeasure data is designed to be much more specific and less vague than paper-based data element definitions.

Module 11 Final Exam – Case Study