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From a financial standpoint, many healthcare leaders consider nursing a cost rather than a potential revenue generator. During a Wolters Kluwer-sponsored HIMSS webinar, nurse leaders discuss the nurse informaticist's contributions in advancing value-based care quantifiably using technology, data collection, and analysis.

During a recent HIMSS webinar, Nurses Count: Classifying Nursing Value with Informatics and Data, sponsored by Wolters Kluwer, top nurse leaders share insights on the developing role of nurse informaticists and their increasing impact on alternative care delivery models and improvements in nursing workflow efficiencies, productivity, and patient outcomes. The one clear message from the webinar? It's time to change how healthcare system administrators calculate nursing's value and impact on patient care.

Distinguished participants include moderator Wolters Kluwer Chief Nurse Anne Dabrow Woods, DNP, RN, CRNP, ANP-BC, AGACNP-BC, FAAN, and panelists Jill Evans, MSN, RN, RN-BC, Associate Chief Nursing Officer, Clinical Informatics, Chief of Connected Care, Virtual Care Enterprise, Metro Health; Nancy Beale, PhD, RN-BC, Chief Nurse Executive/CCIO, Telemetrix; and Connie White Delaney, PhD, RN, FAAN, FACMI, FNAP, Professor and Dean, University of Minnesota School of Nursing.

Question: How has the role of the nursing informaticist evolved over the last 10 years at healthcare systems? What type of impact did the role have during and after the pandemic?

Dr. Beale: "We've continued to spend a lot of the nursing informaticist's effort implementing technologies. But, in addition to just implementation or support of technology, nurses are at that important juncture of taking on the role to be translators understanding the data, the application of artificial intelligence (AI), clinical decision support, and predictive analytics, and using that data in practice and helping clinical practitioners understand how to leverage technology. We've also significantly moved from more informal routes, such as project champions and super users, to more formalized routes to opportunities in the role of an informaticist. Typically, this has been borne out of additional foundational knowledge important to nursing informaticists generally obtained through graduate education."

Dr. Delaney: "To build on Nancy's comment, we truly are translators. The call for more formality and absolute intentionality is deeply with us; and, that call has a critical component of realizing, accepting, and acting on how empowered we are and can be more in the moment toward solutions with a very integrative approach. We have never been in a more empowered position as we are right now regarding nursing visibility and impact on patients' families and community particularly in terms of not only outcomes, but safety, quality, and costs. Perhaps the key realization in this deeply value-oriented world is this recognition of 60% of the healthcare dollar the payments are in value-based models. Living in the value-based payment world is absolutely critical."

Evans: "The role of nursing informaticists continues to grow and is becoming more of an integral part of nursing operations and information technology. One of our evolving technologies is evaluations for the staff where we explain to nurses clearly how it's going to work and benefit them, as well as how it benefits our nursing counterparts on the floors. Our nurses will need to use and interact with AI and any of those pieces and parts [being implemented]. If they don't have a good understanding of what is coming, they won't use nor find value in it. Nursing informaticists, in my organization, have been valuable in explaining to and getting nurses to see how technology enhances and not replaces the work that we're doing and why we all need to use the tools to care for our patients the best way we can."

Question: Most US healthcare systems are struggling with the amount of data and choosing the right data to analyze to determine opportunities for change and process improvement. What is the nursing informaticist's role in data mining and analysis? How can informaticists use the information to improve outcomes for patients?

Dr. Delaney: "All health systems are struggling with the amount of data and choosing the right data to analyze opportunities for change and transformation. One of the primary roles of nursing informaticians is partnering with nursing executives and care providers to maintain that amount of data, meaning we need to continue to boldly lead in determining what type of data, how much, and choosing the right data for the different analytics; and more specifically, beyond leading and envisioning we need to welcome and recognize all the data that are there and create our teams to partner on the data mining and analysis. In nursing, we have phenomenal work going on in big data. In fact, data mining and analysis led through the vision of the nursing lens is quite prolific. Yet, we've barely touched what can all be possible."

Evans: "The one word that comes to me about the nurse informaticist's role is 'interpreter.' They must be able to look at the information, talk to leaders of the organization and nurses at the bedside, explain to them what that data means, and get their feedback. That information allows them to look at the patients they are caring for, look at the performance improvement projects they want to help impact, and ultimately use that data to drive change. If they understand the information they're putting into the electronic health record and their contributions to it, they can better understand how they can make positive change for their patients."

Dr. Beale: "Nurses and nurse informaticists are uniquely positioned to bring the vantage point and nursing science to the equation when we talk about the volumes of data we're collecting in a healthcare setting. We are well positioned to be able to say 'this is the right data to be viewed at the right time in the workflow for clinicians' and then we can recommend what that view should look like in the technology. We know what is important to providing care for the patient both at the bedside and in data review away from the bedside. It's really all in how you present that data. Translating those requirements or requests to the technology teams who are building those views and doing the analysis of the data and ensuring that data is used in the right manner that's where we have an opportunity to bridge technology, computer science, and nursing science together to bring the right data to the clinicians."

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The nurse informaticist role evolves to lead data analytics and ... - Wolters Kluwer

This section describes the data collection process. It starts by describing the design of the experiment and the setup, including a description of the acquisition and processing elements of the methodology.

The experiment was conducted using a machine in which multiple operators interacted through the same HMI to perform a mixture creation task. In this scenario, an industrial mixing machine from the food sector was utilized, which offers the advantage of being regularly used throughout the day by several users across two working shifts. Each time a mixture was ordered, the operator carried out a series of individual interactions with the HMI. These interactions were related to adjusting various parameters, including additive quantity, mixture type, and the use of containers. These parameters directly influenced the properties of the final product.

Users interacted with the machine through a mobile app that was specifically designed for the experiment. Operators accessed the app by scanning a QR code, after which they proceeded to select the required mixture. The captured interactions included two key components: (i) the order and sequence of steps the user followed, and (ii) the time interval in which the user interacted with the machine.

Twenty-seven volunteer operators, aged between 23 and 45 years, participated in the experiment. Each operator granted formal consent to have their daily interactions recorded through the app. In total, 10,608 interactions were captured over a period of 151 days. All data was anonymized and does not contain sensitive user information.

Figure1 illustrates the methodology for data acquisition, which begins with the preparation stage. This stage encompasses two steps: firstly, the user interface (UI) is formally described using a user interface description language (UIDL), which consists of a mark-up language that describes the entire HMI12. In this study, the JSON format was employed to represent each visual element in the HMI, with each element assigned a unique alphanumeric identifier.To provide an example of the UIDL utilized in this study, Fig.2 displays a representation of the UI alongside its corresponding UIDL.

Data acquisition methodology.

UIDL JSON description example of a UI.

The HMI was implemented using Next.js, a React framework and Chakra UI. A dedicated function was created to programmatically generate the HMI using the user interface descriptor. The interface is designed to be responsive and can be used on tactile devices.

Next, the interaction process representation required to prepare a mixture in the machine is described as a Finite State Machine (FSM), which is a model consisting of states, transitions, and inputs used to represent processes or systems. In this process, the user adjusts the parameters of a mixture until the values are considered correct (Fig.3).

Interaction process representation (FSM).

During the active phase of the experiment, when users access the machine using the application, a non-intrusive layer captures the interactions and stores them in a database (capture interactions). The information captured includes the user identity, the timestamp of the interaction in EPOCH format, and the identification of the interacted element (store raw interactions) (see Table1). Once this information is collected, the data processing step generates the sequences.

The goal of this step of the methodology is to generate valid sequences of interactions for each user. Perer & Wang13 define a sequence of events (E=langle {e}_{1},{e}_{2},...,{e}_{m}rangle ) (ei D) as an ordered list of events ei, where D is a set of events known and the order is defined by i. This means that the event ei occurs before the event ei+1. Additionally, in this process is considered that E must contain at least two events e to be accepted as a sequence9.

Using this definition and taking as input the raw interactions, it is possible to define valid interaction sequences as ({s}_{i}=left[{e}_{begin},{e}_{1}^{i},ldots ,{e}_{k}^{i},{e}_{end}right]) where si is a set of events and:

The events ebegin and eend are known, determining the beginning and the ending of the interaction sequence

The variable l determines the length of the interaction sequence and its value should be >=2

The sequences are extracted using the Valid sequences extractor algorithm presented by Reguera-Bakhache et al.9. As demonstrated in the FSM (Fig.3), the interaction process initializes when an interaction occurs in any of the elements that allow the parametrization of the mixture and finalizes when the user clicks the button BTN1OK.

From the 10,608 interactions recorded, 1358 valid sequence interactions were generated. The composition of each interaction sequence is described in the following section.

See original here:

Structured dataset of human-machine interactions enabling adaptive ... - Nature.com

In the fast-evolving landscape of technology, data has become the cornerstone of innovation, and those equipped with the skills to analyze and interpret it are in high demand. Pursuing a Masters in Data Sciencehas emerged as a strategic move for individuals seeking to harness the power of data to drive decision-making processes across industries. This article will explore everything you need to know about earning a Masters in Data Science, from program essentials to crucial concepts like regression in machine learning.The Rising Demand for Data Science Professionals

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Disclaimer: Above mentioned article is a Consumer connect initiative, This article is a paid publication and does not have journalistic/editorial involvement of IDPL, and IDPL claims no responsibility whatsoever.

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Navigating the Path to Mastery: Earning a Masters in Data Science - DNA India