Nursing Intensity of Patient Care Needs and Rates of Healthcare-Associated
Elaine Larson, RN, PhD, CIC, FAAN
Funded by the Agency for Healthcare Research and Quality (1R01HS024915), $1,350,476 awarded September 1, 2016 through August 31, 2019.
While nursing staff are on the front line of preventing and controlling healthcare-associated infections (HAI), studies of the relationship between hospital systems of nursing care and risk of HAI have been limited primarily to assessment of single factors such as staffing or adherence to specific evidence-based guidelines. There are, however, numerous unit- and institutional-level factors that impinge on nursing practice, including the intensity of daily patient care demands and distractions associated with staffing; patient mix and acuity; need for isolation precautions and multiple patient admissions and discharges; and systems-level factors such as the occurrence of emergencies and outbreaks like norovirus, Ebola, tuberculosis (TB), measles, or influenza which divert staff time to emergent planning and response. Over the past decade, major changes related to infection prevention have occurred in federal and state regulations, reimbursement policies, and clinical practice guidelines and therapies. Many of these changes have occurred simultaneously or overlapping in time, but it is not possible to evaluate the impact of these changes on patient outcomes without sufficient validated data regarding relevant structures and processes and HAI rates over an extended time period AND appropriate expertise to handle the complex challenges associated with sophisticated analyses required by with large, complex datasets.
The development and expansion of health information technology holds great promise for improving coordination and standardization of clinical care and health outcomes for individual patients and across health care settings. In addition, the availability of vast amounts of treatment and outcome data make it possible to evaluate on a large scale the effectiveness of systematic changes in policies and practices. Such potential, however, has yet to be fulfilled because data must often be gathered from multiple unlinked sources (e.g., admission, discharge and transfer information; laboratory tests; financial charges; device and other procedure codes; radiology; physician orders and nursing notes; DRG coding; patient location data; surgical procedures; etc.) which do not ‘talk to’ each other. Even within the same healthcare system, data sources are often ‘siloed’ and unlinked. The result is that while huge volumes of patient- and systems-level information are collected, they are not optimally used for outcomes effectiveness research to improve care.
We have developed a database housed as an independent data mart within a Clinical Data Warehouse of ~1 million patient discharges over 9 years (2006-14) from four urban hospitals in a New York City-based large health system (original funding from Health Information Technology to Reduce Healthcare-Associated Infections: HIT-HAI, R01NR010822). The primary goal of this proposed study, Nursing Intensity of Patient Care Needs and Rates of Healthcare-associated Infections (NIC-HAI), is to expand and deploy this large dataset to examine the relationship between HAI and the intensity of nursing demands and needs in acute care at the unit and systems level in order to identify specific interventional targets for future prevention research.
Our specific aims are, in the acute care setting, controlling for confounders and temporal trends, to (1) assess the impact of intensity of nursing care demands and staffing on risk of healthcare-associated infections (HAI) and (2) assess the impact of community onset infectious diseases on HAI including (a) outbreaks of emerging/re-emerging infectious diseases such as influenza A H1N1, Ebola, measles and (b) hospital-based exposures to epidemiologically important community pathogens (e.g., TB, meningitis, scabies, norovirus).
Larson E, Cohen B, Liu J, Zachariah P, Yao D, Shang J. Assessing intensity of nursing care needs using electronically available data. Computers, Informatics, Nursing 2017. Epub ahead of print.