Increasing Medication Administration Safety: Implementation of IV Smart Pump Integration Workflows at the Bedside
Elizabeth Bryand MSN, RN, ACCNS, BC1, Heather Laplume BSN, RN, CDOE2, Michelle Macko MSN, RN3, Jenny Ngin BSN, RN, MEDSURG-BC4, Donalynn Roberts MSN, RN- BC3, Susan Whetstone MSN, RN-BC, NE-BC3
1Newport Hospital, Newport, RI; 2The Miriam Hospital, Providence, RI; 3Lifespan Corporation, Providence, RI; 4Rhode Island Hospital, Providence, RI
Keywords: Smart Pump Integration, EMR, Education, Medication Safety, Technology Workflows
Introduction/BackgroundThe Institute for Safe Medication Practices (ISMP)1 and The Joint Commission (TJC)2 recommend auto-programming of intravenous (IV) infusion pumps to increase medication administration safety by reducing errors related to manual programming. Medication Safety Teams across Lifespan reviewed medication-related events entered in Lifespan’s event reporting platform and identified a pattern of reports related to manual programming errors. These errors included incorrect entry of patient weights, medication doses, medication rates, and medication concentrations. It was proposed that these errors could be eliminated by introducing a new IV infusion pump workflow that utilizes a bi- directional interface of smart pump-EMR integration including infusion start and end times, total volume infused, and rate changes for titratable medications.
MethodsIn January of 2020, a multidisciplinary team was formed. From February 2021 thru March 2021, training of the new workflow was offered to RNs through interactive computer-based training modules and in-person training lab. RN superusers were readily available to support their peers during the go-live dates. Communication via Lifespan’s instant messaging platform also allowed the project team to receive questions from end users 24/7 during the go-live week. The team provided shoulder-to-shoulder support to end users and logged any issues.
ResultsOur systemwide training goal for “in-scope” areas was for 712 RNs to attend the in-person training labs. At go-live, 547 or 77% of those expected had completed the training. All nurses working on units deemed “in scope” for IV smart pump integration were assigned up to three computer-based learning (CBLs). By go-live 53% had completed part one of the required training, 47% completed part two and 44% had completed part three. When looking at pump integration workflow compliance, Lifespan went from 37.5% compliance after the first 2 weeks of go live to 51.0 % compliance between Feb. 14 and 28, 2022. Additionally, the amount of soft limit alerts on pumps have decreased, demonstrating a drop in manual programming by end users. Soft limit alerts with pull back decreased from an average of 434 alerts per month to 281 alerts per month when comparing data six months prior to and post go-live.
Discussion/ConclusionRNs continue to struggle with several aspects of the integration workflow, including data validation of infused volumes into the I&O flowsheet and dose/rate titrations. Ongoing collaboration between Lifespan and the IV smart pump and EMR representatives aim to increase smart pump-EMR integration compliance through workflow optimization and resolution of technical issues, with a compliance goal of 90%.
References
Reducing Specimen Mislabeling Errors Using Lean Six Sigma Danielle Perley, MSN, RN-BC, CPHON; Rose Mintor, MSN, RN-BC, CPN Boston Children's Hospital, Boston, MA Keywords: Specimen Collection, mislabeling, rate reduction, lean six sigma, mobile health devices.
Introduction/BackgroundImproving specimen collection improves patient satisfaction and safety due to the reduced need to have additional labs drawn, decreasing painful procedures and infections related to line accessing.1 A large number of lab specimens are rejected due to mislabeling; laboratory results often direct patient care, and mislabeled specimens result in delayed care, require recollection, and can result in a patient safety event.2 The project goal was to improve the mislabeling rate from 3.5% to 2.6% (25% reduction) post-implementation.
MethodsUtilizing Lean Six Sigma, data were reviewed and analyzed to determine common types of mislabeling. The specimen collection workflow was mapped, identifying key steps to be streamlined. Further, Lean Six Sigma methodologies were used to analyze the voice of the customer and root causes. Based on the data review, it was clear the workflow needed to be improved, and there were quick wins to implement throughout the project. The data analysis showed that in 47% of mislabeled specimens, positive patient identification (PPID) and positive accession identification (PAID) using electronic scanning was bypassed. In the review of the types of mislabeling that was occurring, it was identified that the operational definition of mislabel was not well known by staff; this included using a label with an accession created on a different calendar day then the specimen was collected and if two patients specimens were combined into a single biohazard bag. The project pursued educational signage and workflow changes involving the electronic health record (EHR). The EHR change included initiating a lab accessioning process looking for new routine lab orders every 15 minutes for orders due within an hour. Utilizing this process, a program within the EHR to print labels was eliminated for routine labs, allowing nurses to use a single program for the collection process. It also enabled nurses to print labels while on the go using their mobile devices. This was done for our emergency and inpatient units (not including critical care areas).
ResultsAfter implementation, data from May 2021-September 2021 (5 months) prior to workflow change was compared to October 2021- February 2022 (5 months) post go-live. Prior to implementation, specimen mislabeling rates ranged from 3.4-4.4%. Post-implement specimen mislabeling rates showed a downward trend; ranging consistently between 1.8-2.7%. Importantly, during go-live support, feedback received from end-users was mostly positive. Feedback focused on the ease of the process highlighting the decreased steps and programs to collect samples.
Discussion/ConclusionThis project helped shed light on siloed operational definitions regarding specimen collection among departments. Discrepancies existed between lab processes and policies, which were updated to align at multiple points in the project. Although the new workflow followed our EHR's best practices, buy-in from various information services departments (ISD) and the laboratory teams was required to move forward. The monthly analysis of mislabeled specimens has been transitioned to an existing lab specimen subject matter expert group. The next steps, and likely an additional project, is to evaluate if this process would improve specimen collection in outpatient clinics and infusion centers.
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Implementation of an Enhanced Staffing Grid for Operating Room Daily Assignments
Ingrid Rush MHA, BSN, RN , Nancy Giacomozzi, MEd, BSN, RN-BC, CNOR, Adrienne O’Brien, MSN, RN, CNOR
Boston Medical Center
Keywords: Nursing, Informatics, Information Technology, EPIC, Staffing Software, Operating Room, Staff Assignments, Patient Safety, Satisfaction Introduction/BackgroundIn 2018, we merged two hospital campuses into one with eighteen OR suites. The merge, combining 150 OR staff into one location, triggered new staffing challenges. Knowledge deficits created by unfamiliar specialties and working with unfamiliar staff and physicians caused anxiety. A major challenge was assigning staff to the correct surgical specialty based on skills and preferences, complicated by staff self-scheduling and surgeon block time. The charge nurses took hours assigning staff to OR rooms using recall, which sometimes led to a mismatch in skill-set to case type. The purpose of this project was to improve accuracy, matching staff skills to cases on the daily OR schedule, by providing a visual prompt. Additionally, we aimed to reduce the amount of time to complete manual entry of staff names into the daily OR staffing grid. MethodsThis project was separated into two phases.
Phase I:Decrease the amount of time to complete staff assignments by eliminating manual entry. A daily export from the staffing software to the electronic staffing grid was built to auto-populate two-week schedule blocks of staff names and schedules. Charge nurses view staff names with associated shifts.
Phase II:Improve charge nurses’ accuracy in matching staff to surgical specialties by attaching competencies to each employee and displaying on the staffing grid. Profiles were built for OR staff, and individual competencies were manually entered into the HR software. Staff competencies were exported daily to staffing software and electronic scheduling grid, providing a visual cue. ResultsWe achieved both goals by eliminating manual entry of staff names, and visually displaying staff skills on the OR scheduling grid. Time to complete daily assignments decreased from an average of 180 minutes 90 minutes. Staff skills are better matched to specialties based on assigned competency.
Discussion/ConclusionOur project goals were to decrease burden of completing staff assignments by OR charge nurses by eliminating manual entry into the staffing grid, and match staff skill sets to surgical specialty to improve patient safety.1 We eliminated manual entry and created a visual grid for the charge nurse to complete assignments. Increased staff and surgeon satisfaction was reported from anecdotal comments and positive feedback from our high performance teams. Due to case reduction and staff redeployment to inpatient units during Covid surges we were unable to do a formal satisfaction survey. In 2022 we plan on distributing a satisfaction survey and creating a report to analyze the frequency of staff assignments matching individual skills with the surgical specialty, correlating a decrease or increase in the number of reported surgical errors.
References1. Catchpole, K, Mishra, A, Handa, A, et al. Teamwork and Error in the Operating Room: Analysis of Skills and Roles. Annals of Surgery, 2008 April; 247(4):699-706. Measuring the Impact of Changes in EHR Functionality and Education Interventions on Blood Documentation
Debra Furlong, MS, RN-BC1, Anne Bane, MSN, RN-BC1, Laura Maclean, MS, RN-BC1, Christine Suchecki, MSN, RN-BC2, Theo Abbenthene, MSHI, BSN, RN2, Dan Noar2 David Rubins, MD1,2, Dustin McEvoy21Brigham and Women’s Hospital, Boston, MA; 2MGB Digital Health, Somerville, MA
Keywords: Clinical Documentation, EHR Education, EHR Training, Clinical Decision Support, Documentation improvement, Dual Verification Scanning. Introduction/BackgroundImplementation of our enterprise-wide EHR (eCare) required dual-verification of patient and blood bar code scanning using the Blood Product Administration Module (BPAM). Although we had 99% compliance verifying and scanning blood that was administered non-emergently, Brigham and Women’s Hospital (BWH) a member of the Mass General Brigham-MGB Health Care System had additional requirements that were not as consistently met. We undertook a number of measures to improve in this area. MethodsNursing Informatics staff at BWH worked with report writers to develop a report to identify non-compliance with blood documentation. The blood compliance report was initially run weekly and then transitioned to monthly to monitor elements that were required by blood policy. The results of the blood compliance report were shared with the care units for follow up action. As part of that follow up activity, documents for best practice and tip sheets for blood administration were developed to assist with the education. Additional educational presentations on the correct documentation procedure were presented at unit based-practice councils, Super User sessions, Department Expert meetings and during unit rounds providing one-on-one support. The following changes were implemented in the BPAM build in eCare for all Hospitals in the MGB Health Care System: 1) Blood volume documentation in BPAM window; 2)Blood Administration Report in the Blood Flowsheet 3)Blood Running Banner and Nurse Manager Quality Safety Dashboard Report 4) Interruptive actionable Blood Best Practice Alert 5)“Blood running” indicator on the patient list ResultsImprovement in two of the monitored results “Transfusion Stopped” and “Transfusion Volume” documented on the blood compliance report indicates that build changes resulted in sustained improvement in blood documentation [1]. Transfusion Stopped increased from 84% to 100% and Transfusion Volume Documented increased from 80% to 92%. The BPAM build changes that had an impact were intuitive and within the user’s workflow and required minimal education. Education that was delivered as a reminder to users utilizing tip sheets at departmental meetings, was ongoing during the reporting period, but resulted in little impact in improving blood documentation. In the Blood Donor Center, which had both frequent and a high volume of blood transfusions, one-on-one at-the-elbow training and support was provided to all the users, which resulted in a marked, continued improvement in that area. Discussion/ConclusionContinued monitoring and communication of the results of the Blood Transfusion Report and collaboration with Nursing Informatics colleagues at MGB Digital Health helped to define the need for improvements in the BPAM build. The trended data from the Blood Transfusion report over time correlates with changes to the user interface to the BPAM and focused, directed one on one training in clinical areas that transfused units on a routine basis had significantly more impact than continued follow up with reporting and passive staff education. Nursing informatics plays a key role in assuring users of electronic health records that they have an intuitive user interface for electronic documentation.
References1. Starmer J, Lorenzi N, Pinson CW. The Vanderbilt EvidenceWeb –developing tools to monitor and improve compliance with evidence-based order sets. AMIA Annu Symp Proc 2006: 749-53 Standardized New Patient Electronic Patient Instructions and Nurse Telephone Visit in Infertility DepartmentAmanda Shimko, RN, MS, CNL, Laura Maclean, MS, RN-BC Brigham and Women’s Hospital (BWH), Boston, MA Keywords: Clinical Documentation, Electronic Health Record (EHR), Standardization, Electronic Patient Instructions, Nurse Telephone Visit, Infertility Introduction/BackgroundBWH continues to advance in available resources and access to infertility management within the Center for Infertility and Reproductive Surgery (CIRS). With these advancements, along with the complexity of the specialty, the department has a large nursing presence to work closely with patients and guide them through their fertility care. Upon reviewing current CIRS nursing workflows and patient resources, we noted inconsistencies in processes, instructional content, and structured nursing involvement, especially with new patients. Upon reviewing literature, we discovered the importance of creating close nursing follow up and detailed patient specific instructions following the new patient provider visit to improve patient satisfaction1,2 and reduce repeated communication because of unanswered patient questions.3 Our goals for this project were to develop detailed, clear and consistent content for new electronic patient instructions, improve patient satisfaction and experience, and improve efficiency of patient/staff communication.
MethodsA team from the CIRS department worked in collaboration to develop a standardized workflow and resources for new patients. With use of the new workflow, patients are provided with a general department welcome letter, patient/staff expectations sheet, and appointment confirmation via their patient portal after scheduling their new patient provider visit. Following this visit, the provider uses a standardized checklist functionality in the EHR to send their recommendations for the patient’s initial fertility assessment plan to the nurse. Based on this plan, the nurse sends a patient specific introductory letter to the patient via the portal by selecting the appropriate instructions via a checklist functionality in the EHR. A nurse telephone visit occurs two days after the provider visit to review instructions, answer questions, reiterate patient/staff expectations and complete a learning assessment with the patient. During the pilot phase starting December 2021, two nurses and two providers have utilized this new workflow with all new patients.
ResultsSurveys were distributed to the nurses assessing the usability, efficiency, quality and satisfaction of the new workflow and patient resources pre and post implementation which displayed improvements in all categories. Surveys were also distributed to new patients in both the pilot and non-pilot groups to assess for overall satisfaction, patient experience, and clarity and usefulness of the new instructions and nurse telephone visit. These surveys revealed improvements in all categories. Nurses have also reported a decrease in back-and-forth communication with new patients after initiating this new workflow. More detailed figures and graphs will be shared in the poster presentation.
Discussion/ConclusionA standardized workflow including thorough nursing follow up and detailed patient-specific instructions following a new patient provider visit in a highly specialized department can improve the overall patient experience and understanding of their medical care. With various specialties increasing the focus on care in the ambulatory setting, ambulatory nurses can play a critical role as guides and advocates for patients as they navigate their health journey.
References
Patient Acuity Transparent Classification
Andrea Santos, MSHI, RN, BSN, Sharon Keohane, MBA, RN, BSN, Charlene Feilteau, RN, BSN, Cheryl Dwyer, MS, PMP, Daniel Evans, BSW, Anne Palmgren, RN, Shelly Bazes, MS, RN, CNP, Sherry Cerino, RN, Antigone Grasso, MBAMass General Brigham, Boston, MA
Keywords: Acuity, Transparent Classification, Indicator, Mapping, Interoperability Introduction/BackgroundMass General Brigham (MGB) hospitals and Spaulding Rehab Hospital sites utilize an electronic acuity tool with evidence based clinical indicators to calculate an acuity value which measures patients' needs for direct nursing care.1,2 The information is used for budgeting, creating assignments, and staffing plans, and is a requirement of the Massachusetts ICU staffing law. Historically, nurses manually selected indicators in the application requiring 5-10 minutes daily per nurse. Nurses documented this same data in the patient's electronic health record (EHR). Across MGB, there were differences in how the indicator definitions were applied due to: subjective interpretation of indicator definition and its application, missed indicator selection, and user error in selecting the wrong indicator. Users occasionally missed patient classification, resulting in variation and potential gaps in data for the same or similar patients and patient populations. In 2018, MGB made the decision to implement transparent classification - the interfacing of the clinical documentation to the acuity tool application - to improve efficiencies and data reliability. The indicators would now be objectively applied based upon EHR data, removing the manual selection of indicators, and missed patient classification. MethodsPrior to interoperability, all MGB sites upgraded to the same methodology and product versions. Acuity governance leaders, the Enterprise Project Manager and the MGB clinical documentation Senior Application Coordinator met to conduct and perform an analysis on the data fields and definitions that were current state in the EHR and to map those to the indicators in the acuity tool application. Documentation gaps were identified and addressed through a change management process for the EHR. To achieve interoperability between the third-party vendors required implementation of five net new EHR-to-acuity interfaces. Extensive testing of the interfaces and mapping was performed. Acuity coordinators from each MGB facility and clinical nursing staff were engaged in the testing process. Transparent classification went live for Inpatient on September 29, 2020, and Behavioral Health went live on December 8, 2020. ResultsThere is now greater reliability and consistency of the data because of the shared mapping table. Data is more complete and there is positive feedback from nurses on removing the cognitive and efficiency burdens of redundant documentation. Discussion/ConclusionThere is an ongoing need for evaluation of data integrity and identification of opportunities for improved data capture. The authors strongly recommend early, continuous education of staff regarding the importance of the tool and the data it provides as well as around complete, timely and accurate EHR documentation. References
Implementation of a Process for Sending Patients Home with Take Home Medications in the Emergency DepartmentDonalynn Roberts MSN, RN-BC, Susan Whetstone MSN, NE-BCLifespan, Rhode IslandKeywords:Compliance, Patient Safety, Patient Education Introduction/BackgroundAccording to the Rhode Island Department of Health regulations, a practitioner in a hospital emergency department (ED), hospital clinic or ambulatory surgical center who administers a single dose of medication to a patient from a multi-dose unit of use package, may distribute any remaining doses of the prescription drug to the patient, provided the practitioner gives the patient sufficient instructions regarding the prescription drug. The challenge presented to us, was the lack of interoperability in the Electronic Hospital Record (EHR) to produce a label that could be affixed to the prescribed medication multi-dose package that provided the patient with instructions for use. Medication records in the EHR and printer mapping were not configured to allow this feature. MethodsWorking with an interdisciplinary team across Lifespan, in December 2019, emergency departments in Lifespan began using the “Take Home medication workflow” built within the EHR. After analysis, fifteen medications were identified that would require a new order panel build in the EHR for the take home medications. The identified and approved workflow includes the ED provider entering an order panel which includes three orders, one for the administration of the medication by the ED nurse, one to produce a prescription label for the multi-dose package, and the final order adds the prescribed medication EHR clinical reference to the patient’s After Visit Summary (AVS) upon discharge. ResultsInnovative use of the EHR resulted in a new workflow that fulfills the state requirement resulting in an average of 103 take home medications prescribed per month from the Lifespan Emergency Departments. Patients who are supplied the prescribed medication at discharge from the ED, eliminates the need to fill the prescription at a pharmacy and offers a cost saving for the patient. Although patient satisfaction and readmission rates were not measured as a result of this project, we would anticipate a positive correlation to both rates. The patient leaves the ED with the prescribed medication in hand with accurate administration instructions. The patient does not have to obtain the prescription post discharge from a pharmacy nor pay out of pocket for the medication resulting in the patient being able to take medication as prescribed.
Figure 1: Total Take Home Prescriptions per Lifespan Emergency Department from 12/18 /2019 to 2/28/2022 Discussion/ConclusionThe new workflow fulfills the state requirement, positively impacts patient experience by providing the prescribed medication at discharge and decreases out of pocket expense. The Take Home medication ordering workflow limits waste for the organization. The first dose is administered from the multidose unit, the remaining medication is safely labeled by the provider, then provided to the patient with home administration instructions upon discharge. References:
Targeting and prioritizing gaps in nursing informatics education: an evaluation of student and faculty competence and employer expectations
Andrew B. Phillips PhD, RN, FAMI1,2,3, Roberta Christopher EdD, MSN, APRN, NE-BC, CAIF4,5, Mary K. Kennedy MS RN-BC6, Margie Sipe DNP, RN, NEA-BC, FNAP, FAONL1,3
1 MGH Institute of Health Professions, School of Nursing, Charlestown, Boston, MA.; 2Spaulding Rehabilitation Hospital, Charlestown, MA; 3Massachusetts General Hospital, Boston, MA, 4Keigwin School of Nursing, Watson Caring Science Institute, Boulder, Co; 5Brooks Rehabilitation College of Healthcare Sciences, Jacksonville University, FL; 6Northeastern University, Boston, MA
Keywords: Professional Nursing Education, Clinical Informatics, Clinical Competency, Healthcare Delivery
Introduction/BackgroundNurses represent the largest segment of the healthcare workforce. A majority of international nurse’s associations are troubled that the following drivers are forcing nurses to leave the profession prematurely: heavy workloads, resource shortages, burnout and the stress related to the recent global pandemic.1 Many are anticipating a ‘great resignation’ coupled with the ‘great retirement’. Technology, specifically the burden of documentation associated with electronic health record (E.H.R.), has been identified as an additional source of clinician burnout. Yet “documentation burden remains ill-defined, inconsistently measured” and is a complex issue.2 Nursing has established essential core informatics and healthcare technology competencies for professional nursing education.3 Yet the pace of healthcare information technology innovation and adoption continues to challenge our academic institutions, healthcare delivery organizations and clinicians. This study explores those challenges and gaps from an informatics education perspective.
MethodsThis study assessed the present informatics knowledge of educators and graduating nurses against both identified competencies in the literature and the informatics needs identified by employers hiring nursing graduates. After receiving IRB approval, a mixed methods study that encompassed a quantitative analysis of informatics competencies, using the Nursing Informatics Competency Assessment for Registered Nurses (NICA-RN) for nursing student and faculty participants, was combined with several focus groups with nursing educators and employers. Participants were recruited from across the United States and a variety of academic and clinical settings. Focus group sessions were audio-recorded and transcribed. Content analysis methodologies were used including constant comparison, data immersion, constant questioning (of the data), probing and sorting and negative case analysis.
ResultsNew nurses entering the workforce without access to documenting in a robust simulated or actual electronic health record are at a serious risk for being “left behind.” Educators reported feeling ill prepared to provide robust instruction and integration into their courses. Nurse leaders are expected to create an environment of care that does not result in information and technology overload and are challenged by the pace of innovation.
Discussion/ConclusionAcademic-Clinical partnerships are key to removing barriers and accelerating skill development in digital, eHealth, and informatics competencies for early career nurses as they transition to practice. Executive Nursing leadership, with informatics proficiency, is key in creating structural, personnel and organizational changes to enable digital eHealth. Informatics, as a core competency, needs to be integrated into nursing’s academic preparation and gaps minimized to prepare nurses for the future of healthcare delivery.
References
Using Immersive Virtual Reality Simulation to Study Grocery Shopping Behavior with a Sodium Restricted DietDenise Goldsmith MPH, MS, RN, FAAN, Sara Flash BSNational Institute of Nursing Research, Washington, DC
Keywords: Virtual Reality, Home Care, Self-care Management, Nursing Informatics
IntroductionDigital technologies have dramatically impacted patient care with the advent of electronic medical records, wearable medical devices, and big data analytics. The Advanced Visualization Branch (AVB) of the National Institute of Nursing Research (NINR) evaluates the potential for immersive virtual reality technologies to understand the personal and environmental challenges of homecare and self-management. These technologies offer the potential to study common self-care and home-care activities in a controlled environment.1,2 As robust data collection and evaluation is a critical element of digital research platforms, we are beginning to develop informatics guidance to characterize advanced visualization for research and intervention. We use immersive virtual reality (IVR) to study how people carry out instrumental activities of self-care management, the impact of cognitive challenges and distractors on performance, and the way observations may complement or supplant self-report data.
MethodThe AVB develops interactive IVR simulations that present information to patients with a variety of complex health conditions. These simulations enable observation and measurement of problem-solving behaviors that may impact health outcomes within home-based care environments. This research evaluates digital technologies as a tool to aid in the assessment and management of factors that impact the transition from acute care settings to outpatient environments. Virtual reality environments enable us to build standardized natural settings with experimental controls that would be nearly impossible to perform in real life. We can unobtrusively observe real-time decision making so that the measurement does not perturb the natural behavior of the participant and are able to modify behavior by giving immediate behavioral feedback during intervention trials. We utilize informatics principles of naming conventions, structured information labels and written descriptions of objects, including metadata that supports mapping of virtual object’s physical parameters. Large volumes of data are captured and exported as flat files for later statistical analysis.
ResultsOur first simulation investigates how individuals behave in a virtual grocery store.3 We are inspired by the challenge of assessing patients’ ability to follow sodium restricted diets. In the simulation, the user navigates through a typical grocery store, reads nutrition labels with sodium information, selects food as if for purchase, and places food into a basket. We measure behaviors associated with and indicative of cognitive process, such as frequency of label referencing, product comparison, search strategy and completion of selected tasks common to a shopping trip. At checkout, the selected parameters, such as dietary sodium content, are calculated and users receive visual feedback.
DiscussionWe utilize a consistent approach to presenting sodium content, use formal terms to represent nutritional values and common units of measure. Some of these are used to systematically plan store layout while others are presented to the participant. We apply principles of formalization, standards, and nomenclature to characterize this novel environment. This exploratory work leads us to interesting questions: can we capture and realistically represent environmental conditions that disrupt successful self-care behavior, and can we unobtrusively measure individual differences that contribute to successful outcomes?
References
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