Patricia Flatley Brennan

A consensus action agenda for achieving the national health information infrastructure.

Abstract Background Improving the safety, quality, and efficiency of health care will require immediate and ubiquitous access to complete patient information and decision support provided through a National Health Information Infrastructure (NHII). Methods To help define the action steps needed to achieve an NHII, the U.S. Department of Health and Human Services sponsored a national consensus conference in July 2003. Results Attendees favored a public–private coordination group to guide NHII activities, provide education, share resources, and monitor relevant metrics to mark progress. They identified financial incentives, health information standards, and overcoming a few important legal obstacles as key NHII enablers. Community and regional implementation projects, including consumer access to a personal health record, were seen as necessary to demonstrate comprehensive functional systems that can serve as models for the entire nation. Finally, the participants identified the need for increased funding for research on the impact of health information technology on patient safety and quality of care. Individuals, organizations, and federal agencies are using these consensus recommendations to guide NHII efforts.

Accelerating U.S. EHR Adoption: How to Get There From Here. Recommendations Based on the 2004 ACMI Retreat

Despite growing support for the adoption of electronic health records (EHR) to improve U.S. healthcare delivery, EHR adoption in the United States is slow to date due to a fundamental failure of the healthcare information technology marketplace. Reasons for the slow adoption of healthcare information technology include a misalignment of incentives, limited purchasing power among providers, variability in the viability of EHR products and companies, and limited demonstrated value of EHRs in practice. At the 2004 American College of Medical Informatics (ACMI) Retreat, attendees discussed the current state of EHR adoption in this country and identified steps that could be taken to stimulate adoption. In this paper, based upon the ACMI retreat, and building upon the experiences of the authors developing EHR in academic and commercial settings we identify a set of recommendations to stimulate adoption of EHR, including financial incentives, promotion of EHR standards, enabling policy, and educational, marketing, and supporting activities for both the provider community and healthcare consumers.

The buffering effect of a computer support network on caregiver strain.

ComputerLink was a computer support network for family caregivers of people with Alzheimers disease. In a 12-month experiment, 102 caregivers were randomly assigned to an experimental group that had access to ComputerLink or to a control group that did not. This investigation examined whether caregivers in the experimental group had greater reductions in four types of care-related strain by the end of the 1-year study. ComputerLink reduced certain types of strain if caregivers also had larger informal support networks, were spouses, or did not live alone with their care receivers. More frequent use of the communication function was related to significantly reduced strain for caregivers who were initially more stressed and for non-spouse caregivers. Greater use of the information function was related to significantly lower strain among caregivers who lived alone with care receivers. Overall, ComputerLink appeared to be an effective tool for reducing strain for some caregivers.

Developers and evaluation of interactive health communication applications

BACKGROUND Developers of Interactive Health Communication (IHC) are capable of providing great benefit by creating interactive programs that serve to protect and improve health. Conducting proper evaluation of these programs will ensure that they achieve these goals more successfully. CONCLUSIONS This article seeks to inform developers of IHC about which types of evaluation are most important to include as a part of the development process and to examine the ways in which such evaluation can be implemented to benefit the producers--and ultimately, the consumers--of IHC.

ComputerLink: electronic support for the home caregiver.

Computers have become ubiquitous in contemporary society, as has the demand for home care for the elderly. Caregiving is recognized as a normal experience across the life span, and nurses must develop innovative responses to support caregivers. Computer networks offer caregivers access to a wide range of services such as communication, information, and decision support. Presented here is an interim report of a randomized field experiment demonstrating the feasibility of computer networks as a mechanism for delivering nursing services to caregivers of persons with Alzheimers disease. Caregivers can and do use the computer network in home care.

Use of a home-care computer network by persons with AIDS.

Computer networks serve as convenient, efficient, and enduring vehicles for delivering nursing services to patients at home. The ComputerLink, a specialized computer network, provided nurse-supervised information, decision support, and communication services to home-dwelling persons living with AIDS (PWAs). During a 26-week randomized field experiment, 26 PWAs accessed the ComputerLink on more than 8,664 occasions. The communications area was used most often; the public communication area functioned like a support group. Multiple behavioral measures of use provide a rich picture of how these PWAs, none of whom had had prior computer experience, adopted and adapted to this innovative nursing care delivery system. Meeting the needs of a rapidly growing and diverse population of home-care clients demands that nurses make effective use of existing technologies such as cable television and telephone triage systems. Computer networks combine the best features of cable television and telephone systems--broadcast distribution and interaction; therefore, computer networks represent an ideal technology for the delivery of certain nursing services to the home.

Patient Satisfaction and Normative Decision Theory

This article explores the application of normative decision theory (NDT) to the challenge of facilitating and measuring patient satisfaction. Patient satisfaction is the appraisal, by an individual, of the extent to which the care provided has met that individuals expectations and preferences. Classic decision analysis provides a graphic and computational strategy to link patient preferences for outcomes to the treatment choices likely to produce the outcomes. Multiple criteria models enable the complex judgment task of measuring patient satisfaction to be decomposed into elemental factors that reflect patient preferences, thus facilitating evaluation of care in terms of factors relevant to the individual patient. Through the application of NDT models, it is possible to use patient preferences as a guide to the treatment planning and care monitoring process and to construct measures of patient satisfaction that are meaningful to the individual. Nursing informatics, with its foundations in both information management and decision sciences, provides the tools and data necessary to promote care provided in accord with patient preferences and to ensure appraisal of satisfaction that aptly captures the complex, multidimensional nature of patient preferences.

Assessing data quality: from concordance, through correctness and completeness, to valid manipulatable representations.

The papers by Stein et al.1 and Aronsky and Haug2 address the quality of the data found in clinical record systems. Stein et al. approach the problem as one of internal consistency. Their paper explores concordance within record systems, exploring the extent to which evidence found in one part of a clinical database is consistent with evidence found in another part. Specifically, they examine agreement between entries in a free-text narrative field with data found in coded fields. Aronsky and Haug examine concordance across two different clinical record systems—the HELP computerized clinical record system and a reference standard consisting of the sum of all information available in the paper chart and the computerized clinical record. Aronsky and Haug complement their appraisal of concordance with an outcome evaluation, determining the level of agreement in clinical severity indexes resulting from the information contained in the different record systems. Stein et al. use the measure of internal concordance to alert users of a computerized record system to the fact that they may get misleading answers unless they query each field that might contain a piece of information and resolve any discrepancies. Aronsky and Haug argue the need for equivalence in recommendations based on the clinical record, regardless of which form …

Towards linking patients and clinical information: detecting UMLS concepts in e-mail

The purpose of this project is to explore the feasibility of detecting terms within the electronic messages of patients that could be used to effectively search electronic knowledge resources and bring health information resources into the hands of patients. Our team is exploring the application of the natural language processing (NLP) tools built within the Lister Hill Center at the National Library of Medicine (NLM) to the challenge of detecting relevant concepts from the Unified Medical Language System (UMLS) within the free text of lay peoples electronic messages (e-mail). We obtained a sample of electronic messages sent by patients participating in a randomized field evaluation of an internet-based home care support service to the project nurse, and we subjected elements of these messages to a series of analyses using several vocabularies from the UMLS Metathesaurus and the selected NLP tools. The nursing vocabularies provide an excellent starting point for this exercise because their domain encompasses patients responses to health challenges. In successive runs we augmented six nursing vocabularies (NANDA Nursing Diagnosis, Nursing Interventions Classification, Nursing Outcomes Classification, Home Health Classification, Omaha System, and the Patient Care Data Set) with selected sets of clinical terminologies (International Classification of Primary Care; International Classification of Primary Care- American English; Micromedex DRUGDEX; National Drug Data File; Thesaurus of Psychological Terms; WHO Adverse Drug Reaction Terminology) and then additionally with either Medical Subject Heading (MeSH) or SNOMED International terms. The best performance was obtained when the nursing vocabularies were complemented with selected clinical terminologies. These findings have implications not only for facilitating lay peoples access to electronic knowledge resources but may also be of assistance in developing new tools to aid in linking free text (e.g., clinical notes) to lexically complex knowledge resources such as those emerging from the Human Genome Project.

Nursing Needs Big Data and Big Data Needs Nursing.

PURPOSE Contemporary big data initiatives in health care will benefit from greater integration with nursing science and nursing practice; in turn, nursing science and nursing practice has much to gain from the data science initiatives. Big data arises secondary to scholarly inquiry (e.g., -omics) and everyday observations like cardiac flow sensors or Twitter feeds. Data science methods that are emerging ensure that these data be leveraged to improve patient care. ORGANIZING CONSTRUCT Big data encompasses data that exceed human comprehension, that exist at a volume unmanageable by standard computer systems, that arrive at a velocity not under the control of the investigator and possess a level of imprecision not found in traditional inquiry. Data science methods are emerging to manage and gain insights from big data. METHODS The primary methods included investigation of emerging federal big data initiatives, and exploration of exemplars from nursing informatics research to benchmark where nursing is already poised to participate in the big data revolution. We provide observations and reflections on experiences in the emerging big data initiatives. CONCLUSIONS Existing approaches to large data set analysis provide a necessary but not sufficient foundation for nursing to participate in the big data revolution. Nursings Social Policy Statement guides a principled, ethical perspective on big data and data science. There are implications for basic and advanced practice clinical nurses in practice, for the nurse scientist who collaborates with data scientists, and for the nurse data scientist. CLINICAL RELEVANCE Big data and data science has the potential to provide greater richness in understanding patient phenomena and in tailoring interventional strategies that are personalized to the patient.