The goal of clinical quality measures helps to ensure that the health system is delivering effective, efficient, safe, and patient-centered care at all times. However, the transition of health organization to e-measure is not always smooth and can cause some setbacks https://www.healthcatalyst.com/electronic-clinical-quality-measures-impact-data-quality .
Clinical quality measures are tools used to measure and track the quality of health care services provided by eligible professionals, eligible hospitals, and critical access hospitals within the healthcare system. In recent times, healthcare reporting has been shifting more and more to the use of electronic clinical quality measures mostly referred to as e-measures. These measures use data that shows the health care provider’s ability to meet long-term quality goals. Clinical quality measures help measure various aspects of patient care. Some of these aspects include:
- health outcomes
- clinical processes
- patient safety
- efficient use of healthcare resources
- care coordination
- patient engagements
- population and public health
- adherence to clinical guidelines
Measuring the quality of patient care helps to drive improvements in health care. Specifically, measuring the quality of patient care with CQMs can:
- Identify areas for quality improvement
- Identify differences in care/outcomes among various populations
- Improve care coordination between health care providers
Two main reasons for these setbacks is the inaccuracy of data. Data inaccuracy is a big problem because of the new Inpatient Prospective Payment System (IPPS). While e-measure reporting has been mandatory for Meaningful Use, it has so far been voluntary for other quality reporting programs. This may soon change; in the Inpatient Prospective Payment System (IPPS) proposed rule published in April, CMS is proposing that e-measures be the required reporting method for the HIQRP program as of the calendar year 2016. With this rule, hospitals would have to deal with issues of inaccuracy fast. Now, what are the causes of inaccuracy in data? Two of the main cause are
- Missing Data
- Data Integrity
E-measures are calculated using only the structured data collected in the certified EHR technology (CEHRT). If an e-measure data element is not in the CEHRT, it can skew the accuracy of how the e-measure is calculated. For example, if the date and time a urinary catheter is inserted for an emergency department (ED) patient resides in the ED information system and not in the CEHRT, the EHR will be unable to accurately calculate the relevant Catheter-Associated Urinary Tract Infection (CAUTI) e-measures. To address the problem, healthcare providers may need to create or update several interfaces between the CEHRT and department or specialty modules. Alternatively, organizations using an enterprise data warehouse (EDW) may be able to leverage this tool to create the complete data sets needed to improve e-measure reporting accuracy.
Another possible cause of e-measure inaccuracies is the integrity of the data. This accuracy is often caused by documentation, workflow variation or human error. Take, for example, a scenario in which the hospital EHR is set up to automatically capture a patient’s arrival time as they are being registered with the emergency department. This may seem like an efficient way to collect patient information from the registration workflow but what if the patient is triaged first and then registered? In this case, a change in the workflow produces an inaccurate ED arrival time, which affects the accuracy of any e-measures using this data.
Clinical Quality Measures Privacy vs. the Value of Information
Privacy is very important and everyone values the privacy of their information especially sensitive information like financial information or health information/records. When it comes to compiling healthcare data, patient information can be compiled and used but only after every personal information and details have been stripped away. The healthcare data is then entered into an efficient database. This data and information is then made available for number crunchers who want to see the big picture and don’t need to see the patient as an individual, but as part of a whole. Everyone can benefit from data like this being available, and privacy still being upheld. Back to the thought of not wanting my information out in the hands of those who don’t have a vested interest in my well-being; this progressive achievement in healthcare data availability for all can be in everyone’s best interest. Not only would I be able to visit any doctor or clinic and not have to fill out more paperwork, but I could be admitted into any hospital and those attending to me would not have to assume details of my medical history, but would be able to find if I had any allergies or what medication I was on.
Though these problems exist, the usefulness of electronic clinical quality measures outweighs the negatives. Its advantages will accelerate as EHR technologies and standards continue to mature. A robust data governance program will help healthcare practitioners and health organizations prepare for – and take advantage of – this transition in quality reporting.