Weir
A Taxonomy of Contextual Information . .
A Taxonomy for Contextual Information in Electronic Health Records Charlene R. Weir, RN, PhD1; Nancy Staggers, PhD2, RN, FAAN; Kristina Doing-Harris3, PhD; Robert Dunlea,1 MD; Teresa McCormick1; and Robyn Barrus,1 MS. 1 SLC VA GRECC, Salt Lake City, UT2; School of Nursing, University of Maryland, Baltimore, MD 3 Department of Biomedical informatics, University of Utah, Salt Lake City, UT ABSTRACT Contextual information is functional, social and financial information about patients and is central to many healthcare decisions, including end-of-life care, living arrangements, and the aggressiveness of treatment. It is the language of patients when talking about their health and frequently the focus of nursing interventions. In this study, we report the results of a qualitative analysis of interviews of 17 clinicians focusing on their use of contextual information during the process of care, decision-making and documentation. We identified seven characteristics of contextual information relevant to its use in a clinical setting. Implications for Natural Language Processing and Ontology construction are discussed. INTRODUCTION Contextual information consists of the functional, social and financial information about patients.1 Contextual information is often neglected in clinical decision-making. Yet, functional information is a powerful predictor of patient outcomes while social information can dramatically impact the treatment and placement decisions often made by nurses. Functional status information is central to many health-care decisions, including end-of-life care, living arrangements, and the aggressiveness of treatment. It is the language of patients when talking about their health and frequently the focus of nursing interventions. Clinicians use functional information for a variety of clinical decisions. Perceived functional status was a key predictor of clinicians’ decisions about peri-operative risk for cancer in older adults,2 adjuvant chemo-surgery (social support)3 and discharge plans.4-6 Functional status significantly predicted new Do Not Resuscitate (DNR) orders,7 admission to Intensive Care (ICU) from the Emergency Department (ED),8 9 and transfer to Nursing Home.6 Increasing the availability of contextual information is likely to improve care, enhance decision-making and improve team-work. Smith, et al showed that enhancing access to the patient’s functional information at discharge decreased hospital readmissions.10 Frick, et al. found that clinicians were better at predicting patient’s quality of life and personal goals when they had accurate information about functional status.11 Clinicians significantly underestimated post-acute care needs,12 the degree of frailty in the elderly13 and acute mental status changes.14 For example, ED patients’ functional status was assessed directly by research personnel and correlated with physicians’ decisions to hospitalize or admit to ICU.8 Decisions to hospitalize or admit to ICU were predicted by the physician’s perceptions of the patient age and functional status, but not by their real functional status as measured by researchers. Although contextual information is important, it is either unavailable or difficult to locate in current EHRs.15 Thus, the purpose of this paper is to report on the first stage of a program of research examining the nature of contextual (functional and social) information to support future EHR redesign, Natural Language Processing and/or text searches. Because contextual information is so complex, extracting relevant information requires a deeper understanding of the information itself. Information Theory, Semantic Meaning and Importance of a Taxonomy for Developing an Ontology. Shannon and Weaver’s Information Theory identified 3 levels of information: 1) technical characteristics of information; 2) semantic issues relating to meaning and 3) impact on human behavior.16 Here we focus generally on issues of meaning or the semantic level, using principles of ontology development. A domain-specific ontology is a representation of the language used in a particular domain. These relationships are determined by the semantics (i.e. meaning) associated with each term. Ontology is used in information extraction (IE) to allow the automatic extraction method increased flexibility in term identification, using either synonymy or hierarchical relationships.17 For instance, if the terms “difficulty walking” and “impaired ambulation” are found, the ontology should allow the system to note that they are synonymous; or if the terms “limp” and “impaired gait” are found the ontology should allow the system to note that “limp” is a type of “impaired gait.” When using IE methods to summarize documents, it is important to amalgamate as much redundant information as possible, while tagging oft repeated information as
1
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A Taxonomy of Contextual Information . .
potentially important. Without the ontology it is unlikely this could be accomplished, because people do not generally use the same wording to report the same information, especially across individuals.18 Ontologies can also include attributes associated with each term. In this case it is important to remember that terms may contain multiple words (e.g. “can walk five minutes, without tiring”). As an example, in the context of a social work referral, information supplied by the occupational therapist (OT) may be more important than that supplied by the primary care physician (PC). If the retrieved information has an associated attribute “source,” it is possible to rank OT information above PC information. The ontology is used to delineate which attributes should be collected. METHODS Design: Critical Incident Technique, a form of Cognitive Work Analysis,19 was used to conduct systematic interviews of providers. The interviews were tape-recorded and then analyzed qualitatively. Setting: The Salt Lake City VA Medical Center, a 114-bed hospital, was the study site. Most VA patients have several co-morbid problems and are, thus, very complex. Patients need integrated care with several providers in different departments and contextual information is integral to VA patient care. Three departments most involved in case management were targeted for recruitment: 1) Home Based Primary Care unit – an outpatient home-based primary care clinic, 2) Geriatric Home-base discharge program – a discharge support program for older adults, and 3) SLC VAMC outpatient clinic. We deliberatively chose diverse settings to maximize information gain. Participants: Twenty providers were recruited from the SLC VAMC. Participants had to be case managers and be able to recall a recent difficult transition for a VA patient. Seventeen transcripts were used in the analysis. Two were pilot participants to hone methods and 1 provider was a no-show to the interview. Seven Social Workers, six Nurse Practitioners, two Residents, 1 MD, and 1 Case Manger were enrolled (total 17). We had a wide variety of participants from different specialties and clinics including spinal cord injury group, mental health clinic, acute medicine, and Coumadin clinic. The sample with mean years of experience was 6 nurse practitioners (22), 7 social workers (10), 3 physicians (29) and 1 case worker (4). Procedure: Participants were asked to select a recent patient who was involved in a difficult transition of care, e.g. discharge from the hospital, from the ER, admission from home, etc. The situation required key functional and social information for providers’ decision making. They were asked to open up the patient’s chart during the first part of the interview in order to review and to keep it open for reference. However, the research team did not access patient charts directly. Any identifiable information was stripped during transcription by a medical transcription service formally connected to the VA. Data Analysis Transcriptions of the audio-taped observations, interviews and notes from the observations were analyzed using conventional content analysis techniques.20 Coding protocols were developed iteratively by the research team and recorded in the qualitative software program Atlas ti. The research team developed the coding scheme by highlighting sections of the transcripts believed to be relevant to contextual issues. We assigned short phrases identify the content of the highlighted sections. The descriptive phrases and the original quotations were aggregated into categories/codes through team consensus. A final set of codes was created by team consensus. In this paper, we focus on the information characteristics that were the basis of the taxonomy. RESULTS The results from this initial analysis consisted of an initial taxonomy of the characteristics of contextual information. These characteristics were intended to be as orthogonal as possible in terms of their abstract qualities, but the team discovered that a particular quotation from the interviews could easily be coded with more than one attribute. Table 1 lists the components of the taxonomy with an example from the interviews. Informativeness/Relevance/Vividness: Information clearly varies on how informative and relevant it is to a provider and the particular decision at hand. For example, one of the requirements for placement in an Assisted Living is that a patient be able to leave the building without assistance. Knowing whether a patient is wheel chair bound is highly relevant for that particular decision. Similarly, admitting a wheel-chair dependent 21-yr old veteran to a substance abuse treatment center required explaining that the patient was actually independent in self-care. Another component of in formativeness that appeared in the interviews was the concept of “vividness.” Text written in the words of the patient or embedded in context, e.g. “ the patient crawled to the mailbox every day to get his mail” was talked about
2
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A Taxonomy of Contextual Information . .
as extremely vivid and informative. Vivid information was referred to in a way that seems to “trump” other information in terms of value and allow clinicians to understand a constellation of information about the patient. Table 1. List of taxonomy components and examples. COMPONENT Informativeness/Relevance/ Importance /Vividness Gist/Goals
Temporal Information Source
Legitimacy and Validity
Completeness
Effort/Accessibility
EXAMPLE “The patient crawled to the mailbox every day to get his mail.” “The overall goal of the medical team is to find a solid discharge plan for him where again he won’t be a bounce back because of social issues or because of the high increased needs of health care giving.” “He technically doesn’t have full-blown dementia yet, but he’s definitely getting more and more forgetful.” “Verbally he told me what his goals were. And then I also had background because the doctor who saw him with me knew him for the past four years, until she kind of gave me background as far as what his goals and what his patterns are as far as seeking healthcare.” “The daughter-in-law is probably the least emotionally involved but has the best information.” “She sees what’s going on, so she’s probably one of the best resources for information.” “I asked him why he wasn’t on aspirin. Oh, I don’t know. And then I get some records and find out he had a cerebral bleed which was why he wasn’t given aspirin.” “I don’t know where I would gather that other than I would assume a lot off of that homeless diagnosis.”
Gist/Story/Big picture/Goals. Clinicians needed to understand the basics of why a patient is in a particular situation and derive what clinicians call the “big picture.” For example, a patient’s caregiver died unexpectedly. Knowing his functional status and social information was imperative to understanding the “big picture” and what kind of care or placement would be necessary for the long term. Information that is part of the causal storyline is considered important and information that has clear implications for action, behavior, decision-making for overall care goals provides the best “gist.” In addition, information that changes the goals of care, long-term expected trajectory and rationale about why decisions were made were also considered part of the “big picture.” Temporal. Interviewees appeared to universally ascribe a temporal quality to contextual information and most of the content contained temporal qualifiers. All attributes, whether they were physical, mental, social or financial changed over time and both the current state and the historical state were equally important. For example, clinicians wanted to understand how information changed over time, the rate of change and whether the trend was better/worse. Information Source and Mode. Knowing the source of the information appeared to be as important as the information itself. For example, it was thought to be very important to know if the information was from the clinicians’ direct experience or observation, or from a prior description in the chart. The role of the information source was also important as interviewees appeared to have fairly extensive implicit theories regarding what was included in the notes from different roles e.g. social workers were better sources for social information than physicians. Finally, more formal assessments were given more weight than other sources, such as e-mail. Legitimacy/Validity. Clinicians reported engaging in effortful searches to determine if the information was accurate. Sometimes accuracy could only be determined by the degree to which it was corroborated by other sources, or if it was consistent with medical data and with their own observations. For instance, data from physical therapy mobility test was given greater weight than a functional test or verbal report. Finally, formal assessments might be given more weight than other sources, such as e-mail. Completeness. Completeness refers to how the information fits together and if enough of the detail is provided for a decision. If the patient’s wife is the main care-giver, clinicians immediately need to know how well she is doing at the job, both emotionally and practically. Most clinicians have a schema regarding the relationship of contextual information to a domain. If the patient is close to the end of life, information about patient perspectives are needed.
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A Taxonomy of Contextual Information . .
Effort/Accessibility. Clinicians report having to spend a great deal of time to locate pertinent contextual information. Functional, social and financial information is embedded deep within text notes lacking indexing or tags. Clinicians calculate the cost of locating information and calibrate if it is worth the effort to find. Some functional information is so hard to find that clinicians simply call up another provider or ask the patient themselves rather than looking for it. Another tactic is to expend effort on a first encounter to locate pertinent information and then collate it into a personal clinical note. After that, clinicians only build on the initial note rather than search across others’ documents. The availability of information is clearly a function of both care delivery processes (teamwork) and the characteristics of the EHR. Effort is then a proxy for information accessibility and usability. DISCUSSION The results of this qualitative analysis of clinician’s perceptions and use of contextual information in practice suggest a complex texture to the information characteristics of contextual information. The information itself is weighed and combined by clinicians in a way not reflected in current EHRs. Thus, our initial information taxonomy begins to tease out variables to consider in system redesign, text or NLP searches. Much of the past work in nursing documentation has focused on structured input capturing discrete data such as vital signs, intake and output and other similar patient data. However, contextual information about the patient, such as how family interactions are contributing to the patient’s status, the patient’s emotional condition and the patient’s ability to function in society is often buried in clinical notes in an EHR. In the future, these information categories could be used to code pertinent information for new displays and searches. For instance, the term “wheel-chair bound” should be listed in a patient summary because it implies so much about the patient’s capabilities and care needs. A concise summary of contextual information should be accessible as a summary display and available for quick access including social information, mental and functional status. Implications for NLP and Ontology Development These results are applicable to NLP and ontology development in two ways. In the first, most obvious, instance they can be used to begin to populate the general categories of functional status, financial and social support terms. The classified sentences can be mined to extract words unique to each classification. These words can be integrated into the developing ontology. In the second instance, the characteristics of the information identified in this study can be incorporated into the ontology as attributes of terms. When terms are encountered by human annotators they can, where possible determine the term’s vividness, its relationship to the big picture, if it represents a change over time, its associated source, validity and completeness. Once these attributes have been annotated it may be possible to generate NLP algorithms to populate the term attributes in an automatic recognition system. The populated attributes could then be included as features in a summarization system and used to rank the data returned. The ranking system would be used to present the most important information to the user first. Implications for Collaboration Future research could concentrate on several areas – the extent of information access by other clinicians, the importance of “story-telling” and the effect of language on teamwork. Descriptions of patient’s personal context is largely a “story-telling” endeavor and clinicians feel that they must tell the patient’s story in the best way possible. The characteristics of these stories and the kind of information they include may vary across roles. Nurses, physicians and social workers all use different language to describe issues of patient context as our results demonstrate. Communication between roles is key to high quality of care.21,22 Future work might examine how the language varies, and the impact of this variation on other members of the team. For example, language researchers have investigated what have been called “intergroup frames” and found significant differences in language use between police and citizens23 as well as work on the sociocultural climates in which groups interact.24 CONCLUSIONS This study developed an initial characterization of contextual information in terms of attributes that might be essential to meaning and interpretation. These categories can be used to enhance our understanding of how contextual information is used in practice, both for understanding the patient and for collaboration across roles.
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A Taxonomy of Contextual Information . .
REFERENCES 1.
Weiner S. Contextualizing Medical Decisions to Individualize Care: Lessons from the Qualitative Sciences. J Gen Intern Med. 2004;19:281-285.
2.
Audisio R, Ramesh H, Longo W, Zbar A, D P. Preoperative assessment of surgical risk in oncogeriatric patients. Oncologist. 2005;10(4):262-268.
3.
Bailey C, Corne rJ, Addington-Hall J, Kuma rD, Nelson M, Haviland J. Treatment decisions in older patients with colorectal cancer: the role of age and multidimensional function. Eur J Cancer Care 2003;12(3):257-262.
4.
Bowles K, Fous tJ, MD N. Hospital discharge referral decision making: a multidisciplinary perspective. Appl Nurs Res. 2003;16(3):134-143.
5.
Cykert S, Dilworth-Anderson P, Monroe M, et al. Factors associated with decisions to undergo surgery among patients with newly diagnosed early-stage lung cancer. JAMA. 2010;303(23):2368-2376.
6.
Thraen I, Weir C. A Cognitive Systems Engineering (CSE) Framework for understanding Geriatric “Transfers Processes” across the Healthcare Continuum. under review.
7.
McParland E, Likourezos A, Chichin E, Castor T, Paris B. Stability of preferences regarding life-sustaining treatment: a two-year prospective study of nursing home residents. Mt Sinai J Med. 2003;70(2):85-92.
8.
Rodriguez-Molinero A, Lopez-Diviquez M, Tabuenca A, de la Cr uJ, Banegas J. Physicians' impression on the elders' functionality influences decision making for emergency care. Am J Emerg Med. 2010;757-65(7).
9.
Fisher K, Orkin F, Fraze C. Utilizing conjoint analysis to explicate health care decision making by emergency department nurse. Appl Nurs Res. 2010;1:30-35.
10.
Smith B, Fields C, Fernandez N. Physical therapists make accurate and appropriate discharge recommendations for patients who are acutely ill. Phys Ther. 2010;90(5):693.
11.
Frick S, Uehlinger D, Zuercher-Zenklusen R. Medical futility: predicting outcome of intensive care unit patients by nurses and doctors--a prospective comparative study. Crit Care Med. 2003;3(12):456-457.
12.
Bowles K, Ratcliff eS, Holmes J, Liberatore M, Nydick R, MD N. Post-acute referral decisions made by multidisciplinary experts compared to hospital clinicians and the patients' 12-week outcomes. Med Care. 2008;46(2):158-166.
13.
Boockvar K, Meier D. Palliative care for the frail older adults: "there are things I can't do anymore that I wish I could . . .". JAMA. 2006;296(18):2245-2253.
14.
Husley F, Meldon S. The prevalence and documetnation of impaired mental status in elderly emergency department patients. Ann Emerg Med. 2002;39(3):248-253.
15.
Wilhelmson K, Rubenowitz Lundin E, Andersson C, Sundh V, M W. Interviews or medical records, which type of data yields the best information on elderly people's health status. Aging Clin Exp Res. 2006;1:25-33.
16.
Shannon CE, Weaver W. The Mathematical Theory of Communication, reprinted in 1998. Urbana: University of Illinois Press. ; 1948.
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17.
Adrian B, Hees J, van Elst L, Dengel A. Document: Using Ontologies for Extracting and Annotating Information from Unstructured Text. . Advances in Artificial Intelligence: Lecture notes in Computer Science, volume 5803. 2009:249-256.
18.
Terry A, Chevendra V, Thind A, Stewart M, Marshall J, Cejic S. Using your electronic medical record for research: a primer for avoiding pitfalls. Family Practice. 2010;27:121126.
19.
Flanagan JC. The critical incident technique Psychological Bulletin. 1954;51(4):327-358.
20.
Patton M. Qualitative Research & Evaluation Methods. Thousand Oaks, CA: Sage; 2002.
21.
Coiera E. Clinical communication: a new informatics paradigm. Proc AMIA Annu Fall Symp. 1996:17-21.
22.
Institute of Medicine. To Err is Human. Washington DC: National Academy Press; 2000.
23.
Mallow J, Giles H. Communication, language, and law enforcement: An interfroup ommunication approach. In: Glenn P, LeBaron, C and Mandelbaum, J ed. Studies in Language and Social Interaction. Mahway, NJ: Lawrence Erlbaum Associates; 2002:231-240.
24.
Bourhis RH, Moiese LC, Perreault S, Senecal S. Towards an interactive acculturation model: A social psychological approach. International Journal of the Sociology of Language. 1977;32:369-386.
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A Taxonomy of Contextual Information . .
A Taxonomy for Contextual Information in Electronic Health Records Charlene R. Weir, RN, PhD1; Nancy Staggers, PhD2, RN, FAAN; Kristina Doing-Harris3, PhD; Robert Dunlea,1 MD; Teresa McCormick1; and Robyn Barrus,1 MS. 1 SLC VA GRECC, Salt Lake City, UT2; School of Nursing, University of Maryland, Baltimore, MD 3 Department of Biomedical informatics, University of Utah, Salt Lake City, UT ABSTRACT Contextual information is functional, social and financial information about patients and is central to many healthcare decisions, including end-of-life care, living arrangements, and the aggressiveness of treatment. It is the language of patients when talking about their health and frequently the focus of nursing interventions. In this study, we report the results of a qualitative analysis of interviews of 17 clinicians focusing on their use of contextual information during the process of care, decision-making and documentation. We identified seven characteristics of contextual information relevant to its use in a clinical setting. Implications for Natural Language Processing and Ontology construction are discussed. INTRODUCTION Contextual information consists of the functional, social and financial information about patients.1 Contextual information is often neglected in clinical decision-making. Yet, functional information is a powerful predictor of patient outcomes while social information can dramatically impact the treatment and placement decisions often made by nurses. Functional status information is central to many health-care decisions, including end-of-life care, living arrangements, and the aggressiveness of treatment. It is the language of patients when talking about their health and frequently the focus of nursing interventions. Clinicians use functional information for a variety of clinical decisions. Perceived functional status was a key predictor of clinicians’ decisions about peri-operative risk for cancer in older adults,2 adjuvant chemo-surgery (social support)3 and discharge plans.4-6 Functional status significantly predicted new Do Not Resuscitate (DNR) orders,7 admission to Intensive Care (ICU) from the Emergency Department (ED),8 9 and transfer to Nursing Home.6 Increasing the availability of contextual information is likely to improve care, enhance decision-making and improve team-work. Smith, et al showed that enhancing access to the patient’s functional information at discharge decreased hospital readmissions.10 Frick, et al. found that clinicians were better at predicting patient’s quality of life and personal goals when they had accurate information about functional status.11 Clinicians significantly underestimated post-acute care needs,12 the degree of frailty in the elderly13 and acute mental status changes.14 For example, ED patients’ functional status was assessed directly by research personnel and correlated with physicians’ decisions to hospitalize or admit to ICU.8 Decisions to hospitalize or admit to ICU were predicted by the physician’s perceptions of the patient age and functional status, but not by their real functional status as measured by researchers. Although contextual information is important, it is either unavailable or difficult to locate in current EHRs.15 Thus, the purpose of this paper is to report on the first stage of a program of research examining the nature of contextual (functional and social) information to support future EHR redesign, Natural Language Processing and/or text searches. Because contextual information is so complex, extracting relevant information requires a deeper understanding of the information itself. Information Theory, Semantic Meaning and Importance of a Taxonomy for Developing an Ontology. Shannon and Weaver’s Information Theory identified 3 levels of information: 1) technical characteristics of information; 2) semantic issues relating to meaning and 3) impact on human behavior.16 Here we focus generally on issues of meaning or the semantic level, using principles of ontology development. A domain-specific ontology is a representation of the language used in a particular domain. These relationships are determined by the semantics (i.e. meaning) associated with each term. Ontology is used in information extraction (IE) to allow the automatic extraction method increased flexibility in term identification, using either synonymy or hierarchical relationships.17 For instance, if the terms “difficulty walking” and “impaired ambulation” are found, the ontology should allow the system to note that they are synonymous; or if the terms “limp” and “impaired gait” are found the ontology should allow the system to note that “limp” is a type of “impaired gait.” When using IE methods to summarize documents, it is important to amalgamate as much redundant information as possible, while tagging oft repeated information as
1
Weir
A Taxonomy of Contextual Information . .
potentially important. Without the ontology it is unlikely this could be accomplished, because people do not generally use the same wording to report the same information, especially across individuals.18 Ontologies can also include attributes associated with each term. In this case it is important to remember that terms may contain multiple words (e.g. “can walk five minutes, without tiring”). As an example, in the context of a social work referral, information supplied by the occupational therapist (OT) may be more important than that supplied by the primary care physician (PC). If the retrieved information has an associated attribute “source,” it is possible to rank OT information above PC information. The ontology is used to delineate which attributes should be collected. METHODS Design: Critical Incident Technique, a form of Cognitive Work Analysis,19 was used to conduct systematic interviews of providers. The interviews were tape-recorded and then analyzed qualitatively. Setting: The Salt Lake City VA Medical Center, a 114-bed hospital, was the study site. Most VA patients have several co-morbid problems and are, thus, very complex. Patients need integrated care with several providers in different departments and contextual information is integral to VA patient care. Three departments most involved in case management were targeted for recruitment: 1) Home Based Primary Care unit – an outpatient home-based primary care clinic, 2) Geriatric Home-base discharge program – a discharge support program for older adults, and 3) SLC VAMC outpatient clinic. We deliberatively chose diverse settings to maximize information gain. Participants: Twenty providers were recruited from the SLC VAMC. Participants had to be case managers and be able to recall a recent difficult transition for a VA patient. Seventeen transcripts were used in the analysis. Two were pilot participants to hone methods and 1 provider was a no-show to the interview. Seven Social Workers, six Nurse Practitioners, two Residents, 1 MD, and 1 Case Manger were enrolled (total 17). We had a wide variety of participants from different specialties and clinics including spinal cord injury group, mental health clinic, acute medicine, and Coumadin clinic. The sample with mean years of experience was 6 nurse practitioners (22), 7 social workers (10), 3 physicians (29) and 1 case worker (4). Procedure: Participants were asked to select a recent patient who was involved in a difficult transition of care, e.g. discharge from the hospital, from the ER, admission from home, etc. The situation required key functional and social information for providers’ decision making. They were asked to open up the patient’s chart during the first part of the interview in order to review and to keep it open for reference. However, the research team did not access patient charts directly. Any identifiable information was stripped during transcription by a medical transcription service formally connected to the VA. Data Analysis Transcriptions of the audio-taped observations, interviews and notes from the observations were analyzed using conventional content analysis techniques.20 Coding protocols were developed iteratively by the research team and recorded in the qualitative software program Atlas ti. The research team developed the coding scheme by highlighting sections of the transcripts believed to be relevant to contextual issues. We assigned short phrases identify the content of the highlighted sections. The descriptive phrases and the original quotations were aggregated into categories/codes through team consensus. A final set of codes was created by team consensus. In this paper, we focus on the information characteristics that were the basis of the taxonomy. RESULTS The results from this initial analysis consisted of an initial taxonomy of the characteristics of contextual information. These characteristics were intended to be as orthogonal as possible in terms of their abstract qualities, but the team discovered that a particular quotation from the interviews could easily be coded with more than one attribute. Table 1 lists the components of the taxonomy with an example from the interviews. Informativeness/Relevance/Vividness: Information clearly varies on how informative and relevant it is to a provider and the particular decision at hand. For example, one of the requirements for placement in an Assisted Living is that a patient be able to leave the building without assistance. Knowing whether a patient is wheel chair bound is highly relevant for that particular decision. Similarly, admitting a wheel-chair dependent 21-yr old veteran to a substance abuse treatment center required explaining that the patient was actually independent in self-care. Another component of in formativeness that appeared in the interviews was the concept of “vividness.” Text written in the words of the patient or embedded in context, e.g. “ the patient crawled to the mailbox every day to get his mail” was talked about
2
Weir
A Taxonomy of Contextual Information . .
as extremely vivid and informative. Vivid information was referred to in a way that seems to “trump” other information in terms of value and allow clinicians to understand a constellation of information about the patient. Table 1. List of taxonomy components and examples. COMPONENT Informativeness/Relevance/ Importance /Vividness Gist/Goals
Temporal Information Source
Legitimacy and Validity
Completeness
Effort/Accessibility
EXAMPLE “The patient crawled to the mailbox every day to get his mail.” “The overall goal of the medical team is to find a solid discharge plan for him where again he won’t be a bounce back because of social issues or because of the high increased needs of health care giving.” “He technically doesn’t have full-blown dementia yet, but he’s definitely getting more and more forgetful.” “Verbally he told me what his goals were. And then I also had background because the doctor who saw him with me knew him for the past four years, until she kind of gave me background as far as what his goals and what his patterns are as far as seeking healthcare.” “The daughter-in-law is probably the least emotionally involved but has the best information.” “She sees what’s going on, so she’s probably one of the best resources for information.” “I asked him why he wasn’t on aspirin. Oh, I don’t know. And then I get some records and find out he had a cerebral bleed which was why he wasn’t given aspirin.” “I don’t know where I would gather that other than I would assume a lot off of that homeless diagnosis.”
Gist/Story/Big picture/Goals. Clinicians needed to understand the basics of why a patient is in a particular situation and derive what clinicians call the “big picture.” For example, a patient’s caregiver died unexpectedly. Knowing his functional status and social information was imperative to understanding the “big picture” and what kind of care or placement would be necessary for the long term. Information that is part of the causal storyline is considered important and information that has clear implications for action, behavior, decision-making for overall care goals provides the best “gist.” In addition, information that changes the goals of care, long-term expected trajectory and rationale about why decisions were made were also considered part of the “big picture.” Temporal. Interviewees appeared to universally ascribe a temporal quality to contextual information and most of the content contained temporal qualifiers. All attributes, whether they were physical, mental, social or financial changed over time and both the current state and the historical state were equally important. For example, clinicians wanted to understand how information changed over time, the rate of change and whether the trend was better/worse. Information Source and Mode. Knowing the source of the information appeared to be as important as the information itself. For example, it was thought to be very important to know if the information was from the clinicians’ direct experience or observation, or from a prior description in the chart. The role of the information source was also important as interviewees appeared to have fairly extensive implicit theories regarding what was included in the notes from different roles e.g. social workers were better sources for social information than physicians. Finally, more formal assessments were given more weight than other sources, such as e-mail. Legitimacy/Validity. Clinicians reported engaging in effortful searches to determine if the information was accurate. Sometimes accuracy could only be determined by the degree to which it was corroborated by other sources, or if it was consistent with medical data and with their own observations. For instance, data from physical therapy mobility test was given greater weight than a functional test or verbal report. Finally, formal assessments might be given more weight than other sources, such as e-mail. Completeness. Completeness refers to how the information fits together and if enough of the detail is provided for a decision. If the patient’s wife is the main care-giver, clinicians immediately need to know how well she is doing at the job, both emotionally and practically. Most clinicians have a schema regarding the relationship of contextual information to a domain. If the patient is close to the end of life, information about patient perspectives are needed.
3
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A Taxonomy of Contextual Information . .
Effort/Accessibility. Clinicians report having to spend a great deal of time to locate pertinent contextual information. Functional, social and financial information is embedded deep within text notes lacking indexing or tags. Clinicians calculate the cost of locating information and calibrate if it is worth the effort to find. Some functional information is so hard to find that clinicians simply call up another provider or ask the patient themselves rather than looking for it. Another tactic is to expend effort on a first encounter to locate pertinent information and then collate it into a personal clinical note. After that, clinicians only build on the initial note rather than search across others’ documents. The availability of information is clearly a function of both care delivery processes (teamwork) and the characteristics of the EHR. Effort is then a proxy for information accessibility and usability. DISCUSSION The results of this qualitative analysis of clinician’s perceptions and use of contextual information in practice suggest a complex texture to the information characteristics of contextual information. The information itself is weighed and combined by clinicians in a way not reflected in current EHRs. Thus, our initial information taxonomy begins to tease out variables to consider in system redesign, text or NLP searches. Much of the past work in nursing documentation has focused on structured input capturing discrete data such as vital signs, intake and output and other similar patient data. However, contextual information about the patient, such as how family interactions are contributing to the patient’s status, the patient’s emotional condition and the patient’s ability to function in society is often buried in clinical notes in an EHR. In the future, these information categories could be used to code pertinent information for new displays and searches. For instance, the term “wheel-chair bound” should be listed in a patient summary because it implies so much about the patient’s capabilities and care needs. A concise summary of contextual information should be accessible as a summary display and available for quick access including social information, mental and functional status. Implications for NLP and Ontology Development These results are applicable to NLP and ontology development in two ways. In the first, most obvious, instance they can be used to begin to populate the general categories of functional status, financial and social support terms. The classified sentences can be mined to extract words unique to each classification. These words can be integrated into the developing ontology. In the second instance, the characteristics of the information identified in this study can be incorporated into the ontology as attributes of terms. When terms are encountered by human annotators they can, where possible determine the term’s vividness, its relationship to the big picture, if it represents a change over time, its associated source, validity and completeness. Once these attributes have been annotated it may be possible to generate NLP algorithms to populate the term attributes in an automatic recognition system. The populated attributes could then be included as features in a summarization system and used to rank the data returned. The ranking system would be used to present the most important information to the user first. Implications for Collaboration Future research could concentrate on several areas – the extent of information access by other clinicians, the importance of “story-telling” and the effect of language on teamwork. Descriptions of patient’s personal context is largely a “story-telling” endeavor and clinicians feel that they must tell the patient’s story in the best way possible. The characteristics of these stories and the kind of information they include may vary across roles. Nurses, physicians and social workers all use different language to describe issues of patient context as our results demonstrate. Communication between roles is key to high quality of care.21,22 Future work might examine how the language varies, and the impact of this variation on other members of the team. For example, language researchers have investigated what have been called “intergroup frames” and found significant differences in language use between police and citizens23 as well as work on the sociocultural climates in which groups interact.24 CONCLUSIONS This study developed an initial characterization of contextual information in terms of attributes that might be essential to meaning and interpretation. These categories can be used to enhance our understanding of how contextual information is used in practice, both for understanding the patient and for collaboration across roles.
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A Taxonomy of Contextual Information . .
REFERENCES 1.
Weiner S. Contextualizing Medical Decisions to Individualize Care: Lessons from the Qualitative Sciences. J Gen Intern Med. 2004;19:281-285.
2.
Audisio R, Ramesh H, Longo W, Zbar A, D P. Preoperative assessment of surgical risk in oncogeriatric patients. Oncologist. 2005;10(4):262-268.
3.
Bailey C, Corne rJ, Addington-Hall J, Kuma rD, Nelson M, Haviland J. Treatment decisions in older patients with colorectal cancer: the role of age and multidimensional function. Eur J Cancer Care 2003;12(3):257-262.
4.
Bowles K, Fous tJ, MD N. Hospital discharge referral decision making: a multidisciplinary perspective. Appl Nurs Res. 2003;16(3):134-143.
5.
Cykert S, Dilworth-Anderson P, Monroe M, et al. Factors associated with decisions to undergo surgery among patients with newly diagnosed early-stage lung cancer. JAMA. 2010;303(23):2368-2376.
6.
Thraen I, Weir C. A Cognitive Systems Engineering (CSE) Framework for understanding Geriatric “Transfers Processes” across the Healthcare Continuum. under review.
7.
McParland E, Likourezos A, Chichin E, Castor T, Paris B. Stability of preferences regarding life-sustaining treatment: a two-year prospective study of nursing home residents. Mt Sinai J Med. 2003;70(2):85-92.
8.
Rodriguez-Molinero A, Lopez-Diviquez M, Tabuenca A, de la Cr uJ, Banegas J. Physicians' impression on the elders' functionality influences decision making for emergency care. Am J Emerg Med. 2010;757-65(7).
9.
Fisher K, Orkin F, Fraze C. Utilizing conjoint analysis to explicate health care decision making by emergency department nurse. Appl Nurs Res. 2010;1:30-35.
10.
Smith B, Fields C, Fernandez N. Physical therapists make accurate and appropriate discharge recommendations for patients who are acutely ill. Phys Ther. 2010;90(5):693.
11.
Frick S, Uehlinger D, Zuercher-Zenklusen R. Medical futility: predicting outcome of intensive care unit patients by nurses and doctors--a prospective comparative study. Crit Care Med. 2003;3(12):456-457.
12.
Bowles K, Ratcliff eS, Holmes J, Liberatore M, Nydick R, MD N. Post-acute referral decisions made by multidisciplinary experts compared to hospital clinicians and the patients' 12-week outcomes. Med Care. 2008;46(2):158-166.
13.
Boockvar K, Meier D. Palliative care for the frail older adults: "there are things I can't do anymore that I wish I could . . .". JAMA. 2006;296(18):2245-2253.
14.
Husley F, Meldon S. The prevalence and documetnation of impaired mental status in elderly emergency department patients. Ann Emerg Med. 2002;39(3):248-253.
15.
Wilhelmson K, Rubenowitz Lundin E, Andersson C, Sundh V, M W. Interviews or medical records, which type of data yields the best information on elderly people's health status. Aging Clin Exp Res. 2006;1:25-33.
16.
Shannon CE, Weaver W. The Mathematical Theory of Communication, reprinted in 1998. Urbana: University of Illinois Press. ; 1948.
5
Weir
A Taxonomy of Contextual Information . .
17.
Adrian B, Hees J, van Elst L, Dengel A. Document: Using Ontologies for Extracting and Annotating Information from Unstructured Text. . Advances in Artificial Intelligence: Lecture notes in Computer Science, volume 5803. 2009:249-256.
18.
Terry A, Chevendra V, Thind A, Stewart M, Marshall J, Cejic S. Using your electronic medical record for research: a primer for avoiding pitfalls. Family Practice. 2010;27:121126.
19.
Flanagan JC. The critical incident technique Psychological Bulletin. 1954;51(4):327-358.
20.
Patton M. Qualitative Research & Evaluation Methods. Thousand Oaks, CA: Sage; 2002.
21.
Coiera E. Clinical communication: a new informatics paradigm. Proc AMIA Annu Fall Symp. 1996:17-21.
22.
Institute of Medicine. To Err is Human. Washington DC: National Academy Press; 2000.
23.
Mallow J, Giles H. Communication, language, and law enforcement: An interfroup ommunication approach. In: Glenn P, LeBaron, C and Mandelbaum, J ed. Studies in Language and Social Interaction. Mahway, NJ: Lawrence Erlbaum Associates; 2002:231-240.
24.
Bourhis RH, Moiese LC, Perreault S, Senecal S. Towards an interactive acculturation model: A social psychological approach. International Journal of the Sociology of Language. 1977;32:369-386.
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