OBM Integrative and Complementary Medicine is an international peer-reviewed Open Access journal published quarterly online by LIDSEN Publishing Inc. It covers all evidence-based scientific studies on integrative, alternative and complementary approaches to improving health and wellness.

Topics contain but are not limited to:

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It publishes a variety of article types: original research, review, communication, opinion, case report, study protocol, comment, conference report, technical note, book review, etc.

There is no restriction on paper length, provided that the text is concise and comprehensive. Authors should present their results in as much detail as possible, as reviewers are encouraged to emphasize scientific rigor and reproducibility. 

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Current Issue: 2020  Archive: 2019 2018 2017 2016
Open Access Review
Informatics and Artificial Intelligence Approaches that Promote Use of Integrative Health Therapies in Nursing Practice: A Scoping Review

Sheng-Chieh Lu 1, *, Connie W. Delaney 1, 2, Mary Fran Tracy 1, Chih-Lin Chi 1, 2, Karen A. Monsen 1, 2, 3

1. School of Nursing, University of Minnesota, Minneapolis, Minnesota, USA

2. Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA

3. Earl E. Bakken Center for Spirituality and Healing, University of Minnesota, Minneapolis, Minnesota, USA

Correspondence: Sheng-Chieh Lu

Academic Editor: Kun-Chan Lan and Gerhard Litscher

Special Issue: Artificial Intelligence and Robotic in Complementary and Integrative Medicine

Received: December 30, 2019 | Accepted: February 11, 2020 | Published: February 17, 2020

OBM Integrative and Complementary Medicine 2020, Volume 5, Issue 1, doi:10.21926/obm.icm.2001006

Recommended citation: Lu SC, Delaney CW, Tracy MF, Chi CL, Monsen KA. Informatics and Artificial Intelligence Approaches that Promote Use of Integrative Health Therapies in Nursing Practice: A Scoping Review. OBM Integrative and Complementary Medicine 2020;5(1):22; doi:10.21926/obm.icm.2001006.

© 2020 by the authors. This is an open access article distributed under the conditions of the Creative Commons by Attribution License, which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly cited.

Abstract

Integrative health (IH) therapies are increasingly used to manage health conditions, but barriers hindering nurses from offering IH therapies persist. Informatics and artificial intelligence (AI) approaches have potential to promote nurses’ professional use of IH therapies. The purposes of this review are to (1) describe the state of the science of informatics and AI approaches promoting nurses’ use of IH therapies and (2) identify gaps in literature for future investigations. This systematic scoping review followed the systematic review guideline published by the Centre for Review and Dissemination. Five databases were used to retrieve relevant literature published between 2008 and 2018. Sixteen articles describing fourteen studies met predefined eligibility criteria and were reviewed. A descriptive numerical summary method and thematical analysis were used to synthesize the included literature. A fourfold typology emerged to label the informatics and AI approaches, including robots with AI, computer- and mobile-based applications, electronic communication using an information system, and information standards and standardized terminologies. The reviewed studies suggested that informatics and AI approaches could enhance the safety, accessibility, and communication of nursing IH therapies, as well as enable nursing IH data for IH-related knowledge discovery. Several gaps for future research were identified. There is a need of theory-informed research with more rigorous design and a larger scale to provide robust evidence for implementing existing applications and future innovations. Literature suggests progress toward use of informatics and AI approaches to support nurses’ professional use of IH therapies. Further high-quality and theory-informed research is needed regarding the applications used in conventional healthcare in supporting nurses’ use of IH therapies.

Keywords

Integrative health; informatics approach; artificial intelligence; nursing

1. Introduction

Integrative health (IH) therapies are increasingly utilized as non-pharmacological and patient-centered strategies for managing health conditions. Integrative health therapies are whole person, person-centered, and relationship-based approaches encompassing biomedical and non-biomedical therapies based on scientific evidence for optimal health and well-being [1,2]. Surveys indicated that the prevalence of IH therapy use among adults was 33.2%, 25.9%, and 63.1% in the U.S., European countries, and Australia, respectively [3,4,5]. Integrative health therapies are especially sought by patients with chronic conditions for preventing future health problems, expanding treatment options, and maximizing well-being [6,7,8]. Integrative health therapies have been recommended for managing several health conditions, such as pain, anxiety, and unpleasant side effects of biomedical therapies [9,10,11]. Numerous healthcare institutions have started programs to provide conventional Western medicine in conjunction with IH therapies [12,13].

Nurses, as a healthcare profession embracing a holistic approach, play a central role in the integration of conventional medicine and IH therapies in clinical contexts [12,14]. Studies have shown that nurses are more positive towards IH therapies and willing to learn and offer IH therapies than other professionals [15]. A systematic review reported that 65.9% of nurses, on average, offered IH therapies to their patients [15]. However, research has also revealed several barriers preventing nurses from offering IH therapies. Common themes include lack of knowledge, training, evidence, reliable sources of information, supportive facilities, peer support, time, regulations, and reimbursement [16,17,18,19]. Considerable efforts have been invested in promoting nurses’ professional use of IH therapies [20]. Informatics and artificial intelligence (AI) approaches have potential to facilitate nurses’ professional use of IH therapies through improving information management, supporting care, and capturing care for outcome evaluations [21,22,23].

1.1 Health Informatics Approach

Health informatics is a scientific discipline that integrates health, computer, information, and analytic science to facilitate and support healthcare delivery through managing and communicating data, information, knowledge, and wisdom [24]. Informatics approaches include diverse information- and technology-based innovations that assist healthcare professionals with data collection and processing information for problem-solving, knowledge development, and decision making [24]. Informatics approaches, such as electronic health records (EHR), standardized terminologies, online computer applications, and wireless tracking devices, were highlighted as useful tools for promoting IH therapies use in nursing [25].

The EHR is a promising tool to enhance the quality of nursing documentation and enable nursing data to be used in knowledge discovery and care quality improvement [26,27]. For instance, Johnson and colleagues [28] examined the effectiveness of nurse-delivered aromatherapy in a tertiary hospital using nurse-generated data. They concluded that patients’ symptoms, including pain, anxiety, and nausea, were significantly ameliorated after receiving the therapy [28]. Another study using EHR data to explore the use of acupuncture for musculoskeletal disorder patients showed the prevalence of adverse events related to the therapies [29]. The findings of these studies demonstrate the potential of EHR data for driving knowledge and promoting use of IH therapies in nursing.

Standardized terminologies further enable sharable and comparable nursing data for secondary use [30]. Standardized nursing terminologies (SNTs) are collections of concepts that represent nursing practice in documentation for communication and evaluation of nursing care [30]. The American Nurses Association (ANA) recognizes twelve SNTs supporting nursing documentation, interoperability of nursing data, and reuse of clinical data [31,32]. Previous studies demonstrated the feasibility of SNTs in capturing various nursing practices and enabling nursing data for knowledge discoveries [33]. However, the dominant work related to SNTs has focused on conventional nursing practices. A recent study revealed that Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) captured only 42% of integrative therapy terms [34]. The results highlighted a need for evaluating and expanding existing SNTs to improve the representation, interoperability, and usability of IH therapy data.

Despite limited attention on SNTs for IH therapies, some infrastructural work has been done to prepare the SNTs to capture nursing IH practice. Many evidence-based integrative care guidelines coded with the Omaha System, an ANA-recognized SNT, were developed as referencing resources for practitioners to guide their use of IH therapies and documentation using the Omaha System [35]. These guidelines demonstrate how the Omaha System can be used to represent nursing IH therapies in a structured manner. With structured IH therapy data, a data-driven approach can be applied to evaluate the use and outcomes of nursing IH therapies for inspiring future research and informing clinical and policy decisions [28,29,36,37].

1.2 Artificial Intelligence Applications

Artificial intelligence is a branch of applied computer science that aims to develop computerized systems and robots to perform tasks that typically require human intelligence [38,39]. Several AI and robotic applications have been developed to support healthcare-related tasks, such as AI-aided diagnosis and intervention selections [40]. The techniques have been applied to support professional use of IH therapies. For instance, a robotic application using image recognition techniques was developed to facilitate the localization and stimulation of acupuncture points [22]. Another example is the development of several companion and humanoid robots to facilitate animal-assisted therapies and psychotherapy for patients with cognitive impairments [41]. All these applications demonstrated the applicability of such techniques for promoting use of IH therapies in nursing. Furthermore, researchers have advocated using evidence-informed theoretical frameworks to guide development and evaluation of informatics and AI applications in supporting healthcare [42,43,44]. Such theory-enhanced AI may improve the quality of applications and related data and outcomes [42,43,44].

Although the potential of informatics and AI approaches for promoting nursing IH therapies has been illustrated, the accumulative knowledge has not yet been systematically reviewed and synthesized. The purposes of this scoping review are to (1) describe the state of the science of informatics and AI approaches promoting nurses’ use of IH therapies and (2) identify the gaps in literature for future investigations.

2. Materials and Methods

The current review followed the systematic review guideline published by the Centre for Review and Dissemination [45]. This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews [46].

2.1 Search Strategy

A set of predefined keywords was used to search five electronic databases for identifying literature. The keywords included integrative medicine, integrative nursing, complementary therapy, traditional medicine, alternative therapy, holistic nursing, informatics, information technology, data science, electronic health record, artificial intelligence, and robot, as well as the synonyms of these terms. The databases included the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Ovid MEDLINE, Embase, Scopus, and Google Scholar. The Boolean operators and Medical Subject Heading terms were used to combine the keywords in order to maximize the search. An example of the search strategy used to locate literature in Ovid MEDLINE is provided in Table 1. All searches were limited to the literature published in English between 2008 and 2018. The keywords and search strategies were determined with a senior research librarian. In addition, the chapters related to informatics in two textbooks, including the first and second edition of Integrative Nursing [21,25], were reviewed for additional relevant literature. Furthermore, the citations of all included studies were reviewed to retrieve relevant references.

2.2 Study Selection

Abstracts of each study identified in the search were reviewed before being included. A set of eligibility criteria was used to screen each study. Inclusion criteria were: (1) described or evaluated informatics and AI approaches for promoting nurses’ use of IH therapies during patient care, (2) original research, (3) focused on adult patient care, and (4) published as a full paper through a peer-reviewed process and in English.

Exclusion criteria were: (1) proposed or discussed an informatics and AI approach that did not promoting nurses’ use of IH therapies during patient care, (2) used phone calls, text messages, or emails that were not embedded in an information system to support nurses’ use of IH therapies, (3) published as a review paper, comment, conference abstract, editorial article, thesis, or dissertation, and (4) reported in language other than English.

2.3 Data Extraction

A data extraction tool reflecting the review purposes was developed to extract the relevant data elements from the included literature for evidence summary and synthesis. The data elements included authors, year of publication, study setting, research purpose, types of IH therapy, research design, sampling method, design and theoretical foundations of informatics and AI approach, method for the evaluation of the approach, study results (i.e., sample description, benefit of informatics and AI approach), implications, and limitations. An excel spreadsheet was used to organize the extracted data.

Table 1 Keywords and search strategy for Ovid MEDLINE.

2.4 Data Synthesis

Data from the included articles were synthesized following an analytical framework proposed by Arksey and O'Malley [47]. A descriptive numerical summary method was used to summarize across the included articles for an overview of the current state and focus of the literature. Further, the studies were thematically organized according to different types of informatics and AI approaches.

3. Results

A total of 1,280 unduplicated articles were screened with title and abstract, and 1,224 articles were excluded according to the eligibility criteria. Fifty-six articles were reviewed with full-text, and 16 articles describing 14 studies were included in the current review. The PRISMA flow diagram of the literature screen is shown in Figure 1.

Click to view original image

Figure 1 PRISMA flow diagram of study selection.

3.1 Characteristics of Included Studies

The characteristics of the included articles are summarized in Table 2. Of the 14 studies, six used a quasi-experimental design with one group pre-/post-test or post-test only design [48,49,50,51,52,53], two used retrospective exploratory design [54,55], two used randomize control trial (RCT) [56,57], one used cluster RCT [58,59], one used mixed-method design [14], one used qualitative design [60], and one used cross-sectional design [61]. Most of the studies were conducted in the United States (n= 7), followed by two in Australia, and one each in South Korea, Taiwan, Israel, Norway, and Spain.

For the types of IH therapy discussed in the studies, acupuncture (n=4) and animal-assisted therapy (n=4) were the most frequent IH therapies promoted by informatics and AI approaches, followed by guided imagery (n=2). Other therapies, including herbal and natural products (H/NP), biofeedback, psychotherapy, mind-body therapy, dance therapy, music therapy, acupressure, hypnosis, relaxation techniques, aromatherapy, reflexology, massage, oriental medicine, and traditional Chinese medicine (TCM) were each mentioned in one of the studies.

3.2 Typology of Informatics and AI Approaches

A fourfold typology emerged to label the informatics and AI approaches promoting nurses’ use of IH therapies: robots with AI, computer- and mobile-based applications, electronic communication using an information system, and information standards and standardized terminologies. Definitions and examples of each type of informatics and AI approach are summarized in Table 3 and discussed in the following sections.

3.2.1 Robots with AI

The most prominent approach that emerged in this review is robots with AI that can drive the robots to support IH therapies through perceiving environmental changes and automatically adjusting robot actions. Six studies described and evaluated three different robots promoting animal-assisted therapy, psychotherapy, and biofeedback [53,57,58,59,60,62,63]. Four studies described the use of companion pet robots that could respond to surrounding stimulations through sound and body movements to support animal-assisted therapy for older adults with dementia or cognitive impairments in long-term care facilities [58,59,60,62,63]. Kramer and colleagues [62] compared visitation programs by a person, a person with a live dog, and a person with a robotic dog for residents with dementia in a long-term care facility. The study suggested that the robotic dog could substitute for live dogs since it provided a higher amount of social interaction stimulation and generated fewer concerns about infection, animal care, and injury [62].

Three studies discussed a companion pet robot with the appearance of baby harp seal [58,59,63]. Of them, two RCTs demonstrated the effectiveness of animal-assisted therapy using the robot on maintaining quality of life [59,63] and decreasing symptoms of depression [58] and agitation [58] among older adults with moderate to severe dementia. One study qualitatively evaluating the robot from therapists’ perspectives showed a positive attitude toward offering animal-assisted therapy when used by nurses in their practice [60].

The remaining two studies evaluated humanoid robots supporting psychotherapy and biofeedback [53,57]. Fulmer et al. [57] applied a psychological AI chatbot to deliver individualized interventions rooted in psychosocial modalities for college students to reduce symptoms of depression and anxiety. Their findings suggested that it is feasible and effective to use the chatbot to deliver emotional support using psychotherapy approaches for reducing depressive symptoms among college students [57]. The second study described a robot with biological sensors to facilitate a biofeedback-based training game for physical and cognitive maintenance for older adults [53]. The robot could move its arms to reflect participants’ movements based on the data from a body movement sensor and adjust the difficulty of the game based on heart rate data from another sensor during the game. The study indicated the feasibility and acceptability of the robot in delivering personalized cognitive and motor rehabilitation for older adults [53].

Table 2 Descriptions of the reviewed studies.

Table 3 Description of informatics approaches, example applications, and gaps for future research.

3.2.2 Computer- and Mobile-Based Applications

The second type of approach is computer-and mobile-based applications. Four included studies described and evaluated the use of this approach to promote nurses’ IH therapy use [48,49,50,51,52]. All studies used a quasi-experimental design with one group pre-/post-test or post-test only to evaluate their applications. Of their applications, one was developed as part of an EHR in a medical center to support clinical use of acupuncture. The study showed that their acupuncture treatment system could enhance patient safety by providing order information at the point of care [49]. Another application that was integrated into an EHR was a web-based decision support tool for the use of H/NP. The tool demonstrated the potential to promote safety of H/NP use by empowering nurses in providing the latest evidence [48,51].

The remaining two applications were not built as part of an EHR [50,52]. Of them, one was a mobile-based application for facilitating guided imagery to reduce prenatal stress among hospitalized women at a high risk of preterm birth [50]. In addition to developing new applications, a pre-existing application can be applied to promote IH therapies. Krampe and colleagues [52] applied a pre-existing conference communication software to facilitate a group-based dance therapy for older adults in an assisted living facility. Both applications enabled IH therapies to be delivered remotely and thus potentially enhanced the accessibility of the therapies.

3.2.3 Electronic Communication Using an Information System

Another approach is using electronic communication systems to promote communication and information sharing between nurses and IH practitioners. Two studies described the use and evaluation of this approach [14,55]. However, both studies provided limited descriptions regarding the use, advantages, and disadvantages of their communication systems. Griffin and colleagues [14] explored the data from the consultation system and conducted qualitative interviews with users in a tertiary hospital. Their findings showed that most nurses and physicians were satisfied with the referral process and remarked that the process worked well and was easy to follow [14]. Authors of the second study thematically analyzed the referral data from a medical center and revealed that an electronic consultation system could facilitate communication between IH practitioners and conventional healthcare providers [55].

3.2.4 Information Standards and Standardized Terminologies

The fourth type of approach is the use of information standards and standardized terminologies. The approach was defined as developing, evaluating, and using information standards and standardized terminologies to enable thorough, standardized, interchangeable IH therapy documentation within EHRs. Of the included studies, one study evaluated the usability of an international standards organization reference terminology model (ISO RTM) in representing Oriental nursing interventions in a tertiary Korean hospital [54]. The findings revealed that a standardized terminology developed from a Western medicine perspective was applicable to represent Oriental nursing interventions and enable future research by providing structured data. The authors also indicated that the use of the ISO RTM decreased granularity in describing Oriental nursing interventions [54].

Another included study suggested that an existing information standard based on the biomedical model could promote the interoperability of IH therapy data. Gao and Westra [61] conducted a cross-sectional survey of IH practitioners regarding the relevance of elements in the Continuity of Care Document (CCD) to IH therapy. The study concluded that most CCD elements are relevant and could be used to capture IH interventions and outcomes for information exchange among healthcare settings. However, the study also suggested that a standardized terminology specifically delineating IH therapies should be developed to complement the CCD for sharing information between conventional and IH practitioners [61].

3.3 Gaps Identified in This Review

Serval gaps in the literature were identified for each approach and are summarized in Table 3. In addition, gaps across the approaches were identified. First, none of the included studies discussed the theoretical foundations of their informatics or AI applications and evaluation design. Furthermore, most studies used an observational design or a quasi-experimental design with a single group and a small sample. There is a need of theory-informed research with more rigorous design and larger scale to provide robust evidence for implementing existing applications and future innovations.

4. Discussion

This review of the literature describes the scope of informatics and AI approaches supporting nurses’ professional use of IH therapies to date. The informatics and AI approaches identified in this review were summarized and labeled by a fourfold typology, including robots with AI, computer-and mobile-based applications, electronic communication using an information system, and information standards and standardized terminologies. Informatics and AI approaches may enhance the safety, accessibility, and communication of nursing IH therapies, as well as enable nursing IH data for IH-related knowledge discovery.

The most common type of informatics and AI approach identified in the current review is robots with AI. All studies demonstrated positive results in using AI robots to promote IH therapies. It is notable that there is considerable interest in using robots to support animal-assisted therapy for older adults with dementia and cognitive impairments, consistent with a previous review on the use of robots in nursing [41]. The majority of reviewed studies evaluated their robotic applications from patients’ perspectives or outcomes; additional research is needed regarding the attitude of nursing staff towards robots assisting IH therapy delivery [64]. Further, although studies suggested that pet robots can substitute for live animals, none of the reviewed studies compared robotic and live pets from either patients’ or therapists’ perspectives. Thus, whether robotic pets can replace live animals and bring benefits suggested in the literature [60,62,63] is unknown and should be further examined.

The second type of approach is computer- and mobile-based applications. Four studies described three computer-based software and one mobile-based application for facilitating the use of IH therapies by nurses. All demonstrated feasibility of promoting IH therapies, acceptability to clinicians, enhanced safety of IH therapy use, and increased accessibility of IH services. These advantages are consistent with the benefits of using computer- and mobile-based applications in conventional healthcare [65,66]. However, all four studies used a quasi-experimental design, and the small sample size and single group design may bias the results [67]. More robust methods to test the applications are needed, such as a larger scale experimental and quasi-experimental design with comparison groups and longitudinal follow-up [68]. Furthermore, unintended consequences pertinent to the applications were not explored. Given that computer- and mobile-based applications could have both positive and negative impacts on clinical practice [69], the negative impacts of the applications have potential to offset their benefits and should be investigated.

The third type was electronic communication using an information system used to support nurses’ use of IH therapies. The studies suggested that an electronic consultation system can make the referral smooth and facilitate the communication of patient information and treatments among IH practitioners and nurses. Such improved communication has potential to enable continuity of care, reduce cost by cutting redundant treatment, and improve quality of care [70]. A number of gaps in using the electronic communication system promoting IH therapy communication emerged in this review. First, the primary focus was on analyzing data collected in the systems for discovering knowledge regarding nurses’ decision-making and communication in IH referrals. Descriptions of the design, utilization, and benefits of the systems were limited. Second, both studies were conducted in a tertiary setting. The utilization of an electronic communication system to promote IH communication in other healthcare settings is lacking, despite the fact that primary and community-based care practitioners frequently offer IH therapies to their patients and make IH referrals [71,72]. Whether an electronic communication system can work well for and benefit those care providers needs to be explored in future studies.

The last approach identified in the current review is information standards and standardized terminologies that can enhance the quality, usability, and interoperability of IH therapy documentation. These studies highlighted the need for further development of standardized terminology to support thorough and structured nursing IH intervention documentation. Limited scientific works related to the use of SNTs to enhance IH therapy documentation were identified with the search terms used in this review. None of the reviewed studies examined the representation of the ANA-recognized SNTs in nursing IH therapies. Although use of SNTs to capture IH practice is in its infancy, some work preparing the existing SNTs for documenting IH therapies has been done [21,35]. However, the majority of those works were presented in books and web sites. The extent to which those works can affect the quality and interoperability of nursing IH therapy documentation in real-world practice is unknown and needs to be further explored in the future.

None of the reviewed articles described the theoretical foundations of their approaches, consistent with previous literature urging the need of theoretical approaches to informatics and AI applications supporting healthcare [73]. Previous research has demonstrated that theories provide frameworks for guiding healthcare technology design, implementation, and evaluation [41,42,43]. Informatics and AI approaches for nursing IH therapies should also be developed and evaluated based on evidence-informed theoretical frameworks. Several existing theories could be used to advance the science [44]. For instance, the structure-process-outcome quality of care model [74] or the Problem-Intervention-Outcome Meta-Model [31] could provide theoretical foundations for comprehensive and structural examinations of innovative applications regarding their impact on healthcare and patient outcomes.

The implications of this review are many. Evidence suggests progress toward using informatics and AI applications to address some barriers to nurses’ professional use of IH therapies. For instance, AI robots may facilitate numerous IH therapies and facilitate providing both conventional and IH therapies. Lack of IH-related knowledge may be addressed using computer-and mobile-based applications to provide up-to-date knowledge at the point of care. In addition, all approaches identified were used in conventional healthcare, which may suggest that technology used in conventional nursing has potential to promote IH therapies. However, several technologies, such as expert systems and predictive analytics for nursing outcomes research, are not yet applied to support nursing IH therapies and should be scientifically evaluated.

This review has several limitations. First, the search terms for retrieving literature may fail to capture all relevant literature. Integrative health is not consistently defined in the literature, and numerous terms were used to represent IH therapies [75]. Although the search terms for IH therapies were intentionally broad in this review, investigators may describe IH therapies with other terms. The same is true for informatics and AI approaches. In addition, the feasibility of applying advanced AI applications, such as machine learning techniques, to support IH therapies was demonstrated in previous studies [22,40,76]. However, none of those techniques appeared in this review. Although this absence could be due to the literature databases and search terms used, it may also be true that advanced AI techniques have not been applied to support nurses’ use of IH therapies.

5. Conclusions

The state of the science of informatics and AI approaches that promote nurses’ professional use of IH therapies is that Informatics and AI approaches may enhance the safety, accessibility, and communication related to nursing IH therapies, as well as enabling nursing IH data for knowledge discovery. Informatics and AI approaches have potential to accelerate the integration of conventional and IH therapies for enhancing care quality and continuity of care. Further high-quality and theory-informed research is needed to advance use of informatics and AI in supporting nurses’ use of IH therapies.

Acknowledgments

The authors acknowledge the support of the University of Minnesota Center for Nursing informatics.

Author Contributions

All authors contributed to the design of this study. SCL was involved in acquisition of the papers for review and the data from the papers. SCL and KM initially analyzed the data and wrote the first draft of the manuscript. All authors reviewed and edited the manuscript, as well as approved the final version of the manuscript.

Competing Interests

The authors have no competing interests to declare.

References

  1. Gaboury I, April KT, Verhoef M. A qualitative study on the term CAM: Is there a need to reinvent the wheel? BMC Complement Altern Med. 2012; 12: 131. [CrossRef]
  2. Kreitzer MJ, Koithan M. Integrative nursing. 2nd ed. New York: Oxford University Press; 2018. [CrossRef]
  3. Clarke TC, Black LI, Stussman BJ, Barnes PM, Nahin RL. Trends in the use of complementary health approaches among adults: United States, 2002–2012. Natl Health Stat Report. 2015; 79: 1-16.
  4. Kemppainen LM, Kemppainen TT, Reippainen JA, Salmenniemi ST, Vuolanto PH. Use of complementary and alternative medicine in Europe: Health-related and sociodemographic determinants. Scand J Public Health. 2018; 46: 448-455. [CrossRef]
  5. Steel A, McIntyre E, Harnett J, Foley H, Adams J, Sibbritt D, et al. Complementary medicine use in the Australian population: Results of a nationally-representative cross-sectional survey. Sci Rep. 2018; 8: 1-7. [CrossRef]
  6. Horneber M, Bueschel G, Dennert G, Less D, Ritter E, Zwahlen M. How many cancer patients use complementary and alternative medicine: A systematic review and metaanalysis. Integr Cancer Ther. 2012; 11: 187-203. [CrossRef]
  7. Saydah SH, Eberhardt MS. Use of complementary and alternative medicine among adults with chronic diseases: United States 2002. J Altern Complement Med. 2006; 12: 805-812. [CrossRef]
  8. Wolever RQ, Abrams DI, Kligler B, Dusek JA, Roberts R, Frye J, et al. Patients seek integrative medicine for preventive approach to optimize health. Explor J Sci Heal. 2012; 8: 348-352. [CrossRef]
  9. Institute of Medicine of the National Academies. Relieving pain in America: A blueprint for transforming prevention, care, education, and research. Washington (DC): National Academies Press (US); 2011.
  10. Pan CX, Morrison RS, Ness J, Fugh-Berman A, Leipzig RM. Complementary and alternative medicine in the management of pain, dyspnea, and nausea and vomiting near the end of life: A systematic review. J Pain Symptom Manage. 2000; 20: 374-387. [CrossRef]
  11. The Joint Commission. Joint Commission enhances pain assessment and management requirements for accredited hospitals. The Official Newsletter of The Joint Commission. 2017. p. 477-492.
  12. Cutshall S, Pestka E. Integrating complementary therapies into nursing practice. In: Complementary and alternative therapies in nursing. 8th ed. New York: Springer Publishing Company, LLC; 2018. p. 477-492.
  13. Vohra S, Feldman K, Johnston B, Waters K, Boon H. Integrating complementary and alternative medicine into academic medical centers: Experience and perceptions of nine leading centers in North America. BMC Health Serv Res. 2005; 5: 78. [CrossRef]
  14. Griffin KH, Nate KC, Rivard RL, Christianson JB, Dusek JA. Referrals to integrative medicine in a tertiary hospital: Findings from electronic health record data and qualitative interviews. BMJ Open. 2016; 6: e012006. [CrossRef]
  15. Balouchi A, Mahmoudirad G, Hastings-Tolsma M, Shorofi SA, Shahdadi H, Abdollahimohammad A. Knowledge, attitude and use of complementary and alternative medicine among nurses: A systematic review. Complement Ther Clin Pract. 2018; 31: 146-157. [CrossRef]
  16. Jong M, Lundqvist V, Jong MC. A cross‐sectional study on Swedish licensed nurses' use, practice, perception and knowledge about complementary and alternative medicine. Scand J Caring Sci. 2015; 29: 642-650. [CrossRef]
  17. Kamizato M, Jahana S, Tamai N, Aihara Y, Saeki K, Shimizu K. Nurses’ use of complementary alternative medicine for cancer patients in Japan. J Nurs Care. 2013; 5: 2167-1168.
  18. Metin ZG, Izgu N, Karadas C, Donmez AA. Perspectives of oncology nurses on complementary and alternative medicine in Turkey: A cross-sectional survey. Holist Nurs Pract. 2018; 32: 107-113. [CrossRef]
  19. Zoe R, Charalambous C, Popi S, Maria R, Aris V, Agoritsa K, et al. Cypriot nurses' knowledge and attitudes towards alternative medicine. Complement Ther Clin Pract. 2014; 20: 89-92. [CrossRef]
  20. National Center for Complementary and Integrative Health. NCCIH strategic plan: Exploring the science of complementary and integrative health. 2016. Available from: https://www.nih.gov/about-nih/nih-wide-strategic-plan

  21. Delaney CW, Westra BL, Dean PJ, Leuning CJ, Monsen KA. Informatics and integrative healthcare. In: Integrative nursing. New York: Oxford University Press; 2014. p. 109-121. [CrossRef]
  22. Lan KC, Litscher G. Robot-controlled acupuncture-an innovative step towards modernization of the ancient traditional medical treatment method. Medicines. 2019; 6: 87. [CrossRef]
  23. Lindquist R, Tracy MF, Snyder M. Complementary and alternative therapies in nursing. 8th ed. New York: Springer Publishing Company, LLC; 2018. [CrossRef]
  24. Nelson R, Staggers N. Health Informatics-E-Book: An interprofessional approach. 2nd ed. Elsevier Health Sciences; 2016.
  25. Austin R. Integrative nursing and informatics. In: Integrative nursing. 2nd ed. New York: Oxford University Press; 2018. p. 128-135. [CrossRef]
  26. O'Brien A, Weaver C, Hook ML, Ivory CH. EHR documentation: The hype and the hope for improving nursing satisfaction and quality outcomes. Nurs Adm Q. 2015; 39: 333-339. [CrossRef]
  27. Westra BL, Sylvia M, Weinfurter EF, Pruinelli L, Park JI, Dodd D, et al. Big data science: A literature review of nursing research exemplars. Nurs Outlook. 2017; 65: 549-561. [CrossRef]
  28. Johnson JR, Rivard RL, Griffin KH, Kolste AK, Joswiak D, Kinney ME, et al. The effectiveness of nurse-delivered aromatherapy in an acute care setting. Complement Ther Med. 2016; 25: 164-169. [CrossRef]
  29. Kim MR, Shin JS, Lee J, Lee YJ, Ahn YJ, Park KB, et al. Safety of acupuncture and pharmacopuncture in 80,523 musculoskeletal disorder patients: A retrospective review of internal safety inspection and electronic medical records. Medicine. 2016; 95: e3635. [CrossRef]
  30. Westra BL, Delaney CW, Konicek D, Keenan G. Nursing standards to support the electronic health record. Nurs Outlook. 2008; 56: 258-266. [CrossRef]
  31. Monsen KA. Intervention effectiveness research: Quality improvement and program evaluation. Springer; 2018. [CrossRef]
  32. The Office of the National Coordinator for Health Information Technology. Standard nursing terminologies: A landscape analysis. 2017. Available from: https://www.healthit.gov/sites/default/files/snt_final_05302017.pdf

  33. Tastan S, Linch GC, Keenan GM, Stifter J, McKinney D, Fahey L, et al. Evidence for the existing American Nurses Association-recognized standardized nursing terminologies: A systematic review. Int J Nurs Stud. 2014; 51: 1160-1170. [CrossRef]
  34. Austin R, Lu SC, Delaney CW. Standardization of integrative nursing therapy terms for clinical documentation: A data-driven approach. In: The 3rd International Integrative Nursing Symposium. 2019.
  35. Omaha System Guidelines. Integrative care. 2015. Available from: https://sites.google.com/view/omahasystemguidelines/integrative-care?authuser=0.

  36. Monsen KA, Peterson JJ, Mathiason MA, Kim E, Votava B, Pieczkiewicz DS. Discovering public health nurse–specific family home visiting intervention patterns using visualization techniques. West J Nurs Res. 2017; 39: 127-146. [CrossRef]
  37. Wool C, Kozak LE, Lindley LC. Work environment facilitators to the availability of complementary and alternative therapies in perinatal hospices. J Hosp Palliat Nurs. 2015; 17: 391-396. [CrossRef]
  38. Executive Office of the President. Preparing for the future of artificial Intelligence. Washington D.C: Epub Ahead of Print; 2016.
  39. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019; 25: 30-36. [CrossRef]
  40. Gu S. Reconstructing Chinese Medicine in postmodernity: An artificial intelligence approach. In: Advances in medicine and biology. Hauppauge: Nova Science Publishers; 2019.
  41. Maalouf N, Sidaoui A, Elhajj IH, Asmar D. Robotics in nursing: A scoping review. J Nurs Scholarsh. 2018; 50: 590-600. [CrossRef]
  42. Scott P, Briggs J, Wyatt J, Georgiou A. How important is theory in health informatics? A survey of UK academics. Stud Health Technol Inform. 2011; 169: 223-227.
  43. Wickramasinghe N, Schaffer JL. Theories to inform superior health informatics research and practice. Cham, Switzerland: Springer International Publishing AG; 2016.
  44. Scott PJ, Georgiou A, Hyppönen H, Craven CK, Rigby M, Brender McNair J. Theoretical foundations for evidence-based health informatics: Why? How? Stud Health Technol Inform. 2016; 228: 614-618.
  45. Tacconelli E. Systematic reviews: CRD's guidance for undertaking reviews in health care. Lancet Infect Dis. 2010; 10: 226. [CrossRef]
  46. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Ann Intern Med. 2018; 169: 467-473. [CrossRef]
  47. Arksey H, O'Malley L. Scoping studies: Towards a methodological framework. Int J Soc Res Methodol. 2005; 8: 19-32. [CrossRef]
  48. Boehmer S, Karpa K. Evaluating the value of a web-based natural medicine clinical decision tool at an academic medical center. BMC Health Serv Res. 2011; 11: 279. [CrossRef]
  49. Hou YH, Chang IC, Chen CF. Application of an importance-performance analysis approach to evaluate an acupuncture treatment information system. CIN Comput Informatics Nurs. 2015; 33: 37-42. [CrossRef]
  50. Jallo N, Thacker LR, Menzies V, Stojanovic P, Svikis DS. A stress coping app for hospitalized pregnant women at risk for preterm birth. MCN Am J Matern Nurs. 2017; 42: 257-262. [CrossRef]
  51. Karpa KD, Boehmer S. Examining utilization of a natural medicine Web-based tool among non-primary care physicians, academic faculty, and nurses at one institution. Ann Pharmacother. 2012; 46: 513-520. [CrossRef]
  52. Krampe J, Rubbelke C, Reeves N, Sullivan M, Whitten M. Building evidence: Fuze versus skype for dance-based therapy with older adults and nursing students. CIN Comput Informatics Nurs. 2016; 34: 241-244. [CrossRef]
  53. Lopez-Samaniego L, Garcia-Zapirain B. A robot-based tool for physical and cognitive rehabilitation of elderly people using biofeedback. Int J Enviromental Reasearch Public Heal. 2016; 13: 1176-1189. [CrossRef]
  54. Hwang JI, Park HA. Exploring the usability of the ISO reference terminology model for nursing actions in representing oriental nursing actions. Int J Med Inform. 2009; 78: 656-662. [CrossRef]
  55. Schiff E, Ben-Arye E, Attias S, Sroka G, Matter I, Keshet Y. Perceiving integration of a complementary medicine service within a general surgery department through documentation of consultations: A thematic analysis. Patient Educ Couns. 2012; 89: 430-433. [CrossRef]
  56. Moyle W, Bramble M, Jones C, Murfield J. Care staff perceptions of a social robot called Paro and a look-alike Plush Toy: A descriptive qualitative approach. Aging Ment Health. 2018; 22: 330-335. [CrossRef]
  57. Fulmer R, Joerin A, Gentile B, Lakerink L, Rauws M. Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: Randomized controlled trial. JMIR Ment Health. 2018; 5: e64. [CrossRef]
  58. Jøranson N, Pedersen I, Rokstad AMM, Ihlebæk C. Effects on symptoms of agitation and depression in persons with dementia participating in robot-assisted activity: A cluster-randomized controlled trial. J Am Med Dir Assoc. 2015; 16: 867-873. [CrossRef]
  59. Jøranson N, Pedersen I, Rokstad AMM, Ihlebæk C. Change in quality of life in older people with dementia participating in Paro‐activity: A cluster‐randomized controlled trial. J Adv Nurs. 2016; 72: 3020-3033. [CrossRef]
  60. Birks M, Bodak M, Barlas J, Harwood J, Pether M. Robotic seals as therapeutic tools in an aged care facility: A qualitative study. J Aging Res. 2016; DOI: 10.1155/2016/8569602. [CrossRef]
  61. Gao G, Westra BL. Embracing an integrated personal health record for continuity of care. NI 2012: 11th Int Congr Nurs Informatics, June 23-27, 2012, Montreal, Canada; 2012: American Medical Informatics Association.
  62. Kramer SC, Friedmann E, Bernstein PL. Comparison of the effect of human interaction, animal-assisted therapy, and AIBO-assisted therapy on long-term care residents with dementia. Anthrozoös. 2009; 22: 43-57. [CrossRef]
  63. Moyle W, Cooke M, Beattie E, Jones C, Klein B, Cook G, et al. Exploring the effect of companion robots on emotional expression in older adults with dementia: A pilot randomized controlled trial. J Gerontol Nurs. 2013; 39: 46-53. [CrossRef]
  64. Broadbent E, Kerse N, Peri K, Robinson H, Jayawardena C, Kuo T, et al. Benefits and problems of health‐care robots in aged care settings: A comparison trial. Australas J Ageing. 2016; 35: 23-29. [CrossRef]
  65. Iribarren SJ, Schnall R. mHealth: The intersection of mobile technology and health. In: Health informatics-E-Book: An interprofessional approach. 2nd ed. St. Louis: Elsevier; 2016. p. 255.
  66. Seckman CA. Electronic health records and applications for managing patient care. In: Health Informatics: An interprofessional approach. 2nd ed. St. Louis: Elsevier; 2016, p. 90-110.
  67. Shadish WR, Cook TD, Campbell DT. Statistical conclusion validity and internal validity. Experimental and quasi-experimental designs for generalized causal inference. Belmont: Wadsworth Cengage Learning; 2002. p. 33-63.
  68. Harris AD, McGregor JC, Perencevich EN, Furuno JP, Zhu J, Peterson DE, et al. The use and interpretation of quasi-experimental studies in medical informatics. J Am Med Informatics Assoc. 2006; 13: 16-23. [CrossRef]
  69. Debono DS, Greenfield D, Travaglia JF, Long JC, Black D, Johnson J, et al. Nurses’ workarounds in acute healthcare settings: A scoping review. BMC Health Serv Res. 2013; 13: 175. [CrossRef]
  70. Liddy C, Maranger J, Afkham A, Keely E. Ten steps to establishing an e-consultation service to improve access to specialist care. Telemed J E Health. 2013; 19: 982-990. [CrossRef]
  71. Templeman K, Robinson A. Integrative medicine models in contemporary primary health care. Complement Ther Med. 2011; 19: 84-92. [CrossRef]
  72. Shirwaikar A, Govindarajan R, Rawat AKS. Integrating complementary and alternative medicine with primary health care. Evid Based Complement Alternat Med. 2013; DOI: 10.1155/2013/948308. [CrossRef]
  73. Caronongan A, Gorgui-Naguib H, Naguib RN. The development of intelligent patient-centric systems for health care. In: Theories to inform superior health informatics research and practice. Springer; 2018. p. 355-373. [CrossRef]
  74. Donabedian A. Evaluating the quality of medical care. Milbank Mem Fund Q. 1966; 44: 166-206. [CrossRef]
  75. Holmberg C, Brinkhaus B, Witt C. Experts’ opinions on terminology for complementary and integrative medicine-a qualitative study with leading experts. BMC Complement Altern Med. 2012; 12: 218. [CrossRef]
  76. Chang M, Zhu Q. Automatic location of facial acupuncture-point based on facial feature points positioning. In: Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017). Paris: Atlantis Press; 2017. p. 545-549. [CrossRef]
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