A decision-driven system to collect the patient history

Haug, Peter J.; Warner, Homer R.; Clayton, Paul D.; Schmidt, Candice; Pearl, James; Farney, Robert J.; Frederick, Philip R.

doi:10.1016/0010-4809(87)90045-0

Cited by 16 publications

(4 citation statements)

References 15 publications

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“…• General description: the HELP System, programmed on the Microsoft Query driver, was described in a 1987 publication by Haug et al [17] titled "A Decision-Driven System to Collect the Patient History." Informaticists at the University of Utah described a computer-administered history-taking system with decision-driven questions designed to create a differential diagnosis for hospital inpatients with pulmonary disease.…”

Section: • Help Systemmentioning

confidence: 99%

“…Studies of 22% (4/18) of the tools reported on agreement between tool-produced diagnoses and vignettes with prespecified diagnoses (MEdoctor [28], DocResponse [39,40], Quro [56], and Mandy [57]). Studies of 28% (5/18) of the tools reported agreement between physician-generated diagnoses in health care environments and diagnoses generated by the tools (HELP System [17], AIDA [19], CIDI-Auto [25], Mediktor [36], and DIAANA-AMHTD [58]). The research protocol for an ongoing study of CLEOS was the only article describing measurement of patients' health outcomes.…”

Section: Quality Of Carementioning

confidence: 99%

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Digital Tools Designed to Obtain the History of Present Illness From Patients: Scoping Review

Berdahl¹,

Henreid²,

Pevnick³

et al. 2022

J Med Internet Res

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Background Many medical conditions, perhaps 80% of them, can be diagnosed by taking a thorough history of present illness (HPI). However, in the clinical setting, situational factors such as interruptions and time pressure may cause interactions with patients to be brief and fragmented. One solution for improving clinicians’ ability to collect a thorough HPI and maximize efficiency and quality of care could be to use a digital tool to obtain the HPI before face-to-face evaluation by a clinician. Objective Our objective was to identify and characterize digital tools that have been designed to obtain the HPI directly from patients or caregivers and present this information to clinicians before a face-to-face encounter. We also sought to describe outcomes reported in testing of these tools, especially those related to usability, efficiency, and quality of care. Methods We conducted a scoping review using predefined search terms in the following databases: MEDLINE, CINAHL, PsycINFO, Web of Science, Embase, IEEE Xplore Digital Library, ACM Digital Library, and ProQuest Dissertations & Theses Global. Two reviewers screened titles and abstracts for relevance, performed full-text reviews of articles meeting the inclusion criteria, and used a pile-sorting procedure to identify distinguishing characteristics of the tools. Information describing the tools was primarily obtained from identified peer-reviewed sources; in addition, supplementary information was obtained from tool websites and through direct communications with tool creators. Results We identified 18 tools meeting the inclusion criteria. Of these 18 tools, 14 (78%) used primarily closed-ended and multiple-choice questions, 1 (6%) used free-text input, and 3 (17%) used conversational (chatbot) style. More than half (10/18, 56%) of the tools were tailored to specific patient subpopulations; the remaining (8/18, 44%) tools did not specify a target subpopulation. Of the 18 tools, 7 (39%) included multilingual support, and 12 (67%) had the capability to transfer data directly into the electronic health record. Studies of the tools reported on various outcome measures related to usability, efficiency, and quality of care. Conclusions The HPI tools we identified (N=18) varied greatly in their purpose and functionality. There was no consensus on how patient-generated information should be collected or presented to clinicians. Existing tools have undergone inconsistent levels of testing, with a wide variety of different outcome measures used in evaluation, including some related to usability, efficiency, and quality of care. There is substantial interest in using digital tools to obtain the HPI from patients, but the outcomes measured have been inconsistent. Future research should focus on whether using HPI tools can lead to improved patient experience and health outcomes, although surrogate end points could instead be used so long as patient safety is monitored.

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Section: • Help Systemmentioning

confidence: 99%

Section: Quality Of Carementioning

confidence: 99%

Digital Tools Designed to Obtain the History of Present Illness From Patients: Scoping Review

Berdahl¹,

Henreid²,

Pevnick³

et al. 2022

J Med Internet Res

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“…In the 80s, we identified many trials of software as a substitute for physicians or as a screening tool [26][27][28]. A review from Houziaux defends that such systems were not in use instead, and clinicians were suspicious about it: They believed computers were competitors to be avoided, not assistants.…”

Section: The 1970s and 80smentioning

confidence: 99%

Untitled

2022

IJP

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Introduction: History taking (HT) is the basis for medical practice. Although its format lacks a standard, many HT Instruments (HTI) were developed, applied, and automated throughout history. Objective: To build a history line about HTI development, its pros and cons. Method:We accessed medline and lilacs database through the bvs search engine, using HT equivalents in english, portuguese, french, spanish, and german. Original papers and reviews about HTI aiming general practice were selected, and their content comprehensively analyzed and discussed, following PRISMA guidelines.Results: From 24904 initial entries about HT since 1900, 105 were selected for analysis. First HTI was identified before the 1st World War, mainly as mental screening tools. Later, other general practices HTI were developed, integrating statistical and branching reasoning. Two advantages were consistently reported about clinician time saving and improvement on information gathering. However, their use did not become widespread, restricted to research scenarios and specific guidelines for clinical intervention. Conclusions: HTI benefits may result from ht systematization, and it is not clear if clinical time-saving resultsin economic and quality of care improvement. However, the systematization of HT and the use of computational processing power may help medical practice and should not be overlooked. Better comprehension of the diagnostic HT clinical act will help comprehend how HTI may be useful for clinical practice, reasoning and doctor patient relationship.

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“…To permit a rapid, interactive response-time, their query functions were run on a microcomputer that communicated with their central computer system. The HELP database was also used for alert reports from their laboratory, pharmacy, and radiology subsystems [ 145 ]. Ranum [ 281 ] described their NLP approach to radiology reports that were typically presented in a typewritten format.…”

Section: Querying Medical Textmentioning

confidence: 99%

Development of Medical Information Systems (MISs)

Collen¹,

Hammond

2015

Health Informatics

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By the late 1960s, mainframe-based hospital information systems (HISs) had been developed that could integrate patient data in a single database. In the 1970s, minicomputers made it possible to link the subsystem databases for clinical subspecialties and ancillary services to the mainframe and integrate patient data into the patient records stored there. In the 1980s, microcomputer-based systems that had evolved independently for specialized services became subsystems of larger medical information systems with an integrating central database management system. Storage grew cheaper; registries became databases; databases became data warehouses; and secondary clinical databases were developed. The recognition that databases were equally as important for querying and retrieving data as for documenting care lead to addressing issues of terminologies and other data standards. The fact that much data was unstructured led to the development of natural language processing (NLP) for retrieving and understanding unstructured data. In the 1990s, patient care data expanded in volume and complexity, and innovative clinical information systems offered hospitals and clinics new capabilities. From the 1990s on, the impact of the Internet and the Web grew, enabling global exchange of clinical data and medical knowledge. In the 2000s, distributed information systems allowed physicians using clinical workstations to enter orders and retrieve test results across multiple medical center databases. In the 2010s, federal support greatly increased the use of computer-based patient records. Global wireless communications with cloud storage for translational networks evolved that linked data warehouses in collaborating medical centers nationally and offered mobile e-health care for individual patients. Keywords

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A decision-driven system to collect the patient history

Cited by 16 publications

References 15 publications

Digital Tools Designed to Obtain the History of Present Illness From Patients: Scoping Review

Digital Tools Designed to Obtain the History of Present Illness From Patients: Scoping Review

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Development of Medical Information Systems (MISs)

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