Patient Satisfaction, Outcomes Could Improve with Use of Nursing Quality Analytics Data

Having access to a quality analytics database that measures clinical data specific to the role of nurses as well as data about the nursing environment, could help improve patient satisfaction and outcomes nationwide, according to Christina Dempsey, chief nursing officer at Press Ganey.

Press Ganey, an organization that works to help hospitals and other medical facilities improve patient experiences, recently acquired the National Database of Nursing Quality Indicators (NDNQI) from the American Nursing Association (ANA). NDNQI includes clinical quality measures and corresponding information on nurse engagement and the nursing environment. Press Ganey is distilling the data into four areas that affect the patient experience. They believe this approach will ultimately help their clients decide where to invest in improvements.

“Nurse-sensitive indicators reflect the structure, the process and the outcomes of nursing care,” Dempsey told HealthITAnalytics. “The structure of nursing care is indicated by the supply of the nursing staff, the skill level of the nursing staff, and the education of the nursing staff.  Process indicators measure things like assessments, intervention, and job satisfaction.  And then outcomes are those things that improve if there’s a greater quantity or quality of nursing care, such as pressure ulcers and falls.”

“… Being able to bring some of that data together will allow managers, clinicians, and nurses at the bedside better understand what they need to do for which population of patients to get the highest and best return,” Dempsey said.

Information Technology Management Could Lead to Reduced Medication Errors

In order to reduce medication errors, hospitals could try implementing processes through information technology that will mistake-proof the system. Ultimately, advanced use of technology could replace the tradition of identifying and eliminating nurses and doctors making a high number of medical errors, Search Health IT reports. However, such a high-maintenance technological program may not be right for all institutions.

A growing number of hospitals are training their staff in Lean Six Sigma, a management philosophy developed by Motorola that emphasizes setting extremely high objectives, collecting data, and analyzing results to a fine degree, according to the article. One way that Six Sigma could improve patient outcomes is by preventing the administration of a medication to the wrong patient by building in certain error-proof steps into medication dispensing:

• The patient wristband has a barcode and a location-based tracker.
• The medication administration cart uses location-based tracking and will only dispense the medication after the nurse scans the patient's barcode.
• The medication administration cart displays a photo of the pill and a photo of the patient to serve as a reminder for the nurse.
• The cup that holds the dispensed medication uses location-based tracking to ensure that it is in the correct patient's room.
• The patient's bedside computer has a camera that images the pills and displays the drug name and dosage on the screen so that both the nurse and patient can perform a final check and confirm that the patient is about to receive the proper medication.

While some of these error-proofing steps have already been widely implemented, many hospitals are constrained by lack of access to all of the technologies, or concerns that some steps are time-consuming and would significantly hinder workflow. Using big data analytics, the process of examining large amounts of data to uncover hidden patterns, may provide hospital executives with the information they need to decide on how to both optimize workflow and minimize errors, according to the article.

An INQRI-funded study led by Linda Flynn and Dong-Churl Suh examined the “Impact of Nursing Structures and Processes on Medication Errors.” The multidisciplinary research team identified changes in nursing care processes needed to prevent medication errors as well as adjustments in nurse staffing and the practice environment that can facilitate interception of such errors.

New Briefs Provide Expert Guidance for Ensuring Better State-Level Nursing Workforce Data Systems

An aging population, insurance expansion, increase in chronic disease and new care delivery and financing models are dramatically changing our nation’s health care system. We must have robust and accurate data on the nursing workforce if we are ever to ensure an adequate number nurses to meet the growing demand for health care services created by these forces. Unfortunately the foremost data source on the demographics, location, and practice behaviors of U.S. nurses has been discontinued. Now there is great pressure on states to collect their own nursing workforce data, and to collect common data elements that can be aggregated into a national dataset. This work is complex, costly, and requires significant planning and foresight.

A new set of briefs, developed by a team of researchers funded by INQRI, provides information and guidance to help states build and maintain nursing workforce data systems that will help answer questions about the current and future supply, distribution, diversity, and demand for nurses. The team included: Erin Fraher, PhD, MPP, director of the Program for Health Workforce Research and Policy at the Cecil G. Sheps Center for Health Services Research (Sheps Center), The University of North Carolina at Chapel Hill; Katie Gaul, MA, research associate at the Sheps Center; and Julie Spero, MSPH, research associate at the Sheps Center.

The three briefs address the reasons states should build data systems, how to do it, and what kinds of data should be included.

The first brief explains why states need to build better workforce data systems and identifies the types of questions a robust, well developed, longitudinal nursing workforce data system can help address:

•  How will changing population demographics, economic conditions, and the rapid pace of health systems change affect nursing supply and demand?
•   Are the state’s educational programs producing the workforce needed in the future?
• Does the racial/ethnic, geographic, and specialty distribution of the workforce match population health needs?
• What are the basic demographic and practice characteristics of the state’s nursing workforce and how are they likely to change in the future?

The second brief provides recommendations for creating, organizing, and maintaining a state-level data system, including:

• Forming collaborative partnerships early in the process to determine which entities can best collect, analyze, and report the data, and the best ways to fund and maintain the system.
• Housing the data system with a neutral party to ensure objectivity.
• Considering the possible pitfalls of having the state legislate the development of a system, including the reliance on annual appropriations, limited flexibility in determining questions, and the possibility that data collection and analysis may not remain objective.
• Ensuring adequate funding to cover such cost items as staff time, technology infrastructure, and overhead costs.
• Identifying sustainable, long-term funding sources from the outset of the project.
•   Properly training data system staff to ensure data are protected.

The third brief examines some of the efforts currently underway to create state-level nursing workforce data systems and highlights some of the most promising approaches to collecting data, and determining the kinds of data to collect. The researchers encourage states planning to create nursing workforce data systems to engage in some of these practices, including:

• Using online data collection systems because they lower costs and improve data quality.
• Using in-house information technology expertise to reduce cost, increase flexibility, and promote ownership of the data collection system.
• Ensuring that questions and data values are consistent from year to year.
• Avoiding using open-ended or subjective questions in the data collection process.
• Working with the MDS to ensure consistency without compromising a state’s ability to do longitudinal comparisons.

Sebelius Kicks off "Datapalooza" by Highlighting Use of Health Care Data

At this week's annual "Datapalooza" event, Health and Human Services Secretary Kathleen Sebelius noted that better use of health care data has led to new developments in software and other technologies and has improved patient experiences.

Health Datapalooza is an annual national conference derived from government efforts to liberate health data. This forum features the newest and most innovative and effective uses of health data by companies, startups, academics, government agencies and individuals.

Click here to learn more about Sebelius's remarks.
Click here to learn more about Health Datapalooza.