Biomedical subjects

Anita Patil

Publications and source records attributed to Anita Patil.

5 recordsLinked to original sources

PubMed2005

Using OrgAhead, a computational modeling program, to improve patient care unit safety and quality outcomes.

As part of ongoing research to investigate the impact of patient characteristics, organization characteristics and patient unit characteristics on safety and quality outcomes, we used a computational modeling program, OrgAhead, to model patient care units' achievement of patient safety (medication errors and falls) and quality outcomes. We tuned OrgAhead using data we collected from 32 units in 12 hospitals in Arizona. Validation studies demonstrated acceptable levels of correspondence between actual and virtual patient units. In this paper, we report how we used OrgAhead to develop testable hypotheses about the kinds of innovations that nurse managers might realistically implement on their patient care units to improve quality and safety outcomes. Our focus was on unit-level innovations that are likely to be easier for managers to implement. For all but the highest performing unit (for which we encountered a ceiling effect), we were able to generate practical strategies that improved performance of the virtual units that could be implemented by actual units to improve safety and quality outcomes. Nurse managers have responded enthusiastically to the additional decision support for quality improvement.

Arizona↗

PubMed2004

Using computational modeling to improve patient care unit safety and quality outcomes.

As part of ongoing research to investigate the impact of patient characteristics, organization characteristics and patient unit characteristics on safety and quality outcomes, we are using a computational modeling program, OrgAhead, to model patient care units' achievement of patient safety (medication errors and falls) and quality outcomes. We tuned OrgAhead using data we collected from 16 units in 5 hospitals. Subsequent validation studies demonstrated acceptable levels of correspondence between actual and virtual patient units. In this paper, we report on our initial efforts to use OrgAhead to develop testable hypotheses about the kinds of innovations that nurse managers might realistically implement on their patient care units to improve quality and safety outcomes. Our focus is on unit-level innovations that are likely to be easier for managers to implement. For all but the highest performing unit (for which we encountered a ceiling effect), we were able to generate practical strategies that improved performance of the virtual units by 6-8 percentage points. Nurse Managers have responded enthusiastically to the additional decision support for quality improvement

Accidental Falls↗

PubMed2003

Using computational modeling to study the impact of workplace characteristics on patient safety outcomes.

How do patient characteristics, organization characteristics and patient care unit characteristics interact to affect quality, safety, and cost outcomes? What changes can nurse managers make on their units that will optimize outcomes for their patients? To answer these questions, we are collecting data from 35 nursing units in 12 hospitals in Arizona, and using the results as a basis for computational modeling. Although it has been used in clinical research, until now computational modeling has had little application to healthcare or nursing organizations. In this poster session, we describe our application of Orgahead, a computational modeling program.

Models, Nursing↗

PubMed2002

Teaching evidence-based medicine skills through a residency-developed guideline.

Though many residencies have recognized the need for instruction in evidence-based medicine, the best way to teach these skills to resident physicians remains uncertain. We designed a curriculum intended to develop a resident-produced, evidence-based guideline for the care of patients with diabetes. Each resident was supervised going through the steps of evidence-based medicine: asking a clinical question, searching for the evidence to answer that question, appraising that evidence, and producing an evidence-based answer. These answers were then compiled into a guideline distributed in the residency practice. An evaluation of this curriculum using focus group and survey data showed that learners appreciated the skills and knowledge gained in devising guidelines in an evidence-based manner but were uncertain that their searches were complete. The clinical evaluation of the guideline implementation showed improvement in several clinical markers of diabetes care.

Curriculum↗

PubMedDate not supplied

Using computational modeling to transform nursing data into actionable information.

Transforming organizational research data into actionable information nurses can use to improve patient outcomes remains a challenge. Available data are numerous, at multiple levels of analysis, and snapshots in time, which makes application difficult in a dynamically changing healthcare system. One potential solution is computational modeling. We describe our use of OrgAhead, a theoretically based computational modeling program developed at Carnegie Mellon University, to transform data into actionable nursing information. We calibrated the model by using data from 16 actual patient care units to adjust model parameters until performance of simulated units ordered in the same way as observed performance of the actual units 80% of the time. In future research, we will use OrgAhead to generate hypotheses about changes nurses might make to improve patient outcomes, help nurses use these hypotheses to identify and implement changes on their units, and then measure the impact of those changes on patient outcomes.

Computational Biology↗