David S. Pieczkiewicz

Data visualization techniques to showcase nursing care quality

K KAREN A. MONSEN, PhD, RN, FAAN JESSICA J. PETERSON, MS, RN, CRNA MICHELLE A. MATHIASON, MS ERA KIM, MS SEONAH LEE, PhD CHIN-LIN CHI, PhD, MBA DAVID S. PIECZKIEWICZ, PhD School of Nursing (Drs Monsen and Chi and Mss Mathiason and Peterson), Institute for Health Informatics (Drs Monsen, Chi, and Pieczkiewicz and Ms Kim), andCenter for Nursing Informatics, University of Minnesota–Twin Cities and Omaha System Partnership, Minneapolis (Dr Monsen); Mayo Clinic Health System–Franciscan Healthcare, La Crosse, WI (Ms Peterson); and St Louis College of Nursing, University of Missouri (Dr Lee).

Towards prevention of acute syndromes: electronic identification of at-risk patients during hospital admission.

BACKGROUND Identifying patients at risk for acute respiratory distress syndrome (ARDS) before their admission to intensive care is crucial to prevention and treatment. The objective of this study is to determine the performance of an automated algorithm for identifying selected ARDS predisposing conditions at the time of hospital admission. METHODS This secondary analysis of a prospective cohort study included 3,005 patients admitted to hospital between January 1 and December 31, 2010. The automated algorithm for five ARDS predisposing conditions (sepsis, pneumonia, aspiration, acute pancreatitis, and shock) was developed through a series of queries applied to institutional electronic medical record databases. The automated algorithm was derived and refined in a derivation cohort of 1,562 patients and subsequently validated in an independent cohort of 1,443 patients. The sensitivity, specificity, and positive and negative predictive values of an automated algorithm to identify ARDS risk factors were compared with another two independent data extraction strategies, including manual data extraction and ICD-9 code search. The reference standard was defined as the agreement between the ICD-9 code, automated and manual data extraction. RESULTS Compared to the reference standard, the automated algorithm had higher sensitivity than manual data extraction for identifying a case of sepsis (95% vs. 56%), aspiration (63% vs. 42%), acute pancreatitis (100% vs. 70%), pneumonia (93% vs. 62%) and shock (77% vs. 41%) with similar specificity except for sepsis and pneumonia (90% vs. 98% for sepsis and 95% vs. 99% for pneumonia). The PPV for identifying these five acute conditions using the automated algorithm ranged from 65% for pneumonia to 91 % for acute pancreatitis, whereas the NPV for the automated algorithm ranged from 99% to 100%. CONCLUSION A rule-based electronic data extraction can reliably and accurately identify patients at risk of ARDS at the time of hospital admission.

Discovering Public Health Nurse–Specific Family Home Visiting Intervention Patterns Using Visualization Techniques

Visualization is a Big Data method for detecting and validating previously unknown and hidden patterns within large data sets. This study used visualization techniques to discover and test novel patterns in public health nurse (PHN)–client–risk–intervention–outcome relationships. To understand the mechanism underlying risk reduction among high risk mothers, data representing complex social interventions were visualized in a series of three steps, and analyzed with other important contextual factors using standard descriptive and inferential statistics. Overall, client risk decreased after clients received personally tailored PHN services. Clinically important and unique PHN–client–risk–intervention–outcome patterns were discovered through pattern detection using streamgraphs, heat maps, and parallel coordinates techniques. Statistical evaluation validated that PHN intervention tailoring leads to improved client outcomes. The study demonstrates the importance of exploring data to discover ways to improve care quality and client outcomes. Further research is needed to examine additional factors that may influence PHN–client–risk–intervention–outcome patterns, and to test these methods with other data sets.

Evaluating the decision accuracy and speed of clinical data visualizations

Clinicians face an increasing volume of biomedical data. Assessing the efficacy of systems that enable accurate and timely clinical decision making merits corresponding attention. This paper discusses the multiple-reader multiple-case (MRMC) experimental design and linear mixed models as means of assessing and comparing decision accuracy and latency (time) for decision tasks in which clinician readers must interpret visual displays of data. These tools can assess and compare decision accuracy and latency (time). These experimental and statistical techniques, used extensively in radiology imaging studies, offer a number of practical and analytic advantages over more traditional quantitative methods such as percent-correct measurements and ANOVAs, and are recommended for their statistical efficiency and generalizability. An example analysis using readily available, free, and commercial statistical software is provided as an appendix. While these techniques are not appropriate for all evaluation questions, they can provide a valuable addition to the evaluative toolkit of medical informatics research.

The Role of Nonverbal and Verbal Communication in a Multimedia Informed Consent Process

OBJECTIVE Nonverbal and verbal communication elements enhance and reinforce the consent form in the informed consent process and need to be transferred appropriately to multimedia formats using interaction design when re-designing the process. METHODS Observational, question asking behavior, and content analyses were used to analyze nonverbal and verbal elements of an informed consent process. RESULTS A variety of gestures, interruptions, and communication styles were observed. CONCLUSION In converting a verbal conversation about a textual document to multimedia formats, all aspects of the original process including verbal and nonverbal variation should be one part of an interaction community-centered design approach.

The influence of display format on decision-making in a lung transplant home monitoring program-preliminary results

Transplant recipients participating in the lung transplant home monitoring program at the University of Minnesota use portable electronic spirometers to record daily pulmonary function parameters and symptom information, which is reviewed weekly by a team of clinicians for early signs of infection and/or rejection. We present the preliminary results from experiments designed to determine whether the display format of the information presented to clinicians influences decision accuracy or time for this detection task. In two experiments, clinicians were shown randomly-ordered displays of patient home monitoring data, presented in a variety of formats (line graphs, tables, hybrid graph-tables, and control charts), and media (paper or computer screen) and asked to render probability-based decisions on whether the data indicated a possible infection/rejection event. Decision time and display preferences were also recorded. Results indicated no statistically significant difference in accuracies or times between formats in either experiment, though a possible trend favoring graphical displays was observed in both measures. Readers also tended to prefer the graphical displays. We conclude that screen-based, graphical displays of pulmonary data would be well-accepted, efficacious tools in clinical practice.

Redesigning an Information System that Reduces Health Care Accessibility Effort and Increases User Acceptance and Satisfaction: A Comparative Effectiveness Study

Objectives: This research tackles a critical issue in modern health care systems—namely, to determine if creating a user-centered health information system that is easy to utilize would lead to consumers who are more satisfied and more likely to accept the system. Materials and Methods: The health information system is a consumer service center that receives inquiries from consumers on how to find and pay for care. To understand if a system designed to decrease effort results in satisfaction, we redesigned the system, deployed it for 3 months, and then compared consumer satisfaction results to a control group. Satisfaction and Net Promoter surveys were provided to consumers who used the control system and consumers using the redesigned system. Results: This study was completed over a 6 month continual time period where over 100,000 consumer interactions took place. Using 11 different metrics and data from over 5,000 random system users, it was shown that consumers were more satisfied with an information system designed to reduce their administrative effort. Discussion: While not all consumer survey results were statistically significant, they all showed a shift towards improved satisfaction with the health care system. Statistically, it was shown that there was a dependency between the design of the system to provide information and many needs of the consumers. Conclusion: A health care system designed to reduce effort in accessing care results in improved consumer satisfaction. Consumers are also more likely to trust the assistance provided by the organization.

Assessment of Personal Health Care Management and Chronic Disease Prevalence: Comparative Analysis of Demographic, Socioeconomic, and Health-Related Variables

Background The use of personal health care management (PHM) is increasing rapidly within the United States because of implementation of health technology across the health care continuum and increased regulatory requirements for health care providers and organizations promoting the use of PHM, particularly the use of text messaging (short message service), Web-based scheduling, and Web-based requests for prescription renewals. Limited research has been conducted comparing PHM use across groups based on chronic conditions. Objective This study aimed to describe the overall utilization of PHM and compare individual characteristics associated with PHM in groups with no reported chronic conditions, with 1 chronic condition, and with 2 or more such conditions. Methods Datasets drawn from the National Health Interview Survey were analyzed using multiple logistic regression to determine the level of PHM use in relation to demographic, socioeconomic, or health-related factors. Data from 47,814 individuals were analyzed using logistic regression. Results Approximately 12.19% (5737/47,814) of respondents reported using PHM, but higher rates of use were reported by individuals with higher levels of education and income. The overall rate of PHM remained stable between 2009 and 2014, despite increased focus on the promotion of patient engagement initiatives. Demographic factors predictive of PHM use included people who were younger, non-Hispanic, and who lived in the western region of the United States. There were also differences in PHM use based on socioeconomic factors. Respondents with college-level education were over 2.5 times more likely to use PHM than respondents without college-level education. Health-related factors were also predictive of PHM use. Individuals with health insurance and a usual place for health care were more likely to use PHM than individuals with no health insurance and no usual place for health care. Individuals reporting a single chronic condition or multiple chronic conditions reported slightly higher levels of PHM use than individuals reporting no chronic conditions. Individuals with no chronic conditions who did not experience barriers to accessing health care were more likely to use PHM than individuals with 1 or more chronic conditions. Conclusions The findings of this study illustrated the disparities in PHM use based on the number of chronic conditions and that multiple factors influence the use of PHM, including economics and education. These findings provide evidence of the challenge associated with engaging patients using electronic health information as the health care industry continues to evolve.

An Evaluation of Overcoming Barriers to Engage Consumers in the Use of Health Care Information Technology

ABSTRACT The purpose of this review is to determine why consumers may not be adopting or engaging in the use of health information technology to successfully improve or maintain their health status. A literature search was completed to find articles related to consumer engagement in the use of health information technology. The literature found was then categorized based on a patient engagement framework defined by the National eHealth Collaborative. The barriers to engagement, issues with types of technology, and problems in study methodology were then synthesized for understanding consumer engagement. The results of the review showed the major barriers related to engaging consumers in the use of health information technology, including privacy, education, cost, literacy, accuracy of information, trust, meeting consumer needs, measures, integration, and policy. The most prevalent of these issues is that health information technology is often not designed with accurate and detailed user requirements in mind. Issues of the methodologies used to show how consumer health information technology is consumed relate to studies that are not aligned with the original purpose of the experiment. Additional work and research is needed to ensure that the design of consumer health information technology meets the consumer’s needs. Improved designs and methods of achieving these designs will create technology that consumers find appealing to use in order to better manage their health. If consumers find value in the technology, they will be more likely to engage and adopt it for additional use.

Using Publicly Available Data to Characterize Consumers Use of Email to Communicate with Healthcare Providers.

The use of patient focused technology has been proclaimed as a means to improve patient satisfaction and improve care outcomes. The Center for Medicaid/Medicare Services, through its EHR Incentive Program, has required eligible hospitals and professionals to send and receive secure messages from patients in order to receive financial incentives and avoid reimbursement penalties. Secure messaging between providers and patients has the potential to improve communication and care outcomes. The purpose of this study was to use National Health Interview Series (NHIS) data to identify the patient characteristics associated with communicating with healthcare providers via email. Individual patient characteristics were analyzed to determine the likelihood of emailing healthcare providers. The use of email for this purpose is associated with educational attainment, having a usual place of receiving healthcare, income, and geography. Publicly available data such as the NHIS may be used to better understand trends in adoption and use of consumer health information technologies.