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Our topic is The Future of Healthcare. We are now exposed to many different ways of learning and processing information. This new direction will allow us to access new cures for many different diseases and will allow better ways for us to provide better patient care. We have compiled several articles and videos that will show you a glimpse of what the future holds in healthcare.

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The Future of Healthcare through Nursing Informatics

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The Future of Healthcare through Nursing Informatics

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Group # 2
Michael Jones - Quantum Computing
Melissa Mangieri - Voice Activated Communicators
Emily Manke - Mobile devices
Dawn Legarth Maxin - Virtual Reality
Niall Mcgarry - Game and Simulation Technology

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Das, R. (2016, October 12). Ten Top Technologies That Will Transform The Healthcare Industry. Retrieved May 24, 2019, from https://www.forbes.com/sites/reenitadas/2016/10/11/healthcare-2025-ten-top-technologies-that-will-transform-the-industry/#7b3ce2f03e18

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Quantum Computing is the wave of the future. Instead of analyzing 1 or 2 things at a time this process will help compute everything.

Solenov, D., Brieler, J., & Scherrer, J. F. (2018, September/October). The Potential of Quantum Computing and Machine Learning to Advance Clinical Research and Change the Practice of Medicine. Retrieved May 24, 2019, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6205278/

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Dmitry Solenov, PhD

Jay Brieler, MD

Jeffrey F. Scherrer, PhD

Introduction

We may be at the advent of a revolution in computer applications in clinical care and medical research. Quantum computing can exponentially advance computational power and promises to usher in a new epoch in computing technology. Quantum computing compared to current computing could be what the first personal computer was to Charles Babbage's 1833 primitive computer the 'difference engine".

A Patient Encounter in the Age of Quantum Computing

Ms. Jones is at her annual follow-up for long term cancer screening. She had tissue sparing breast cancer surgery 20 years ago and has since been cancer free. Today she goes to the high speed imaging center and meets with her physician, Dr. Doe, 20 minutes after the scan. Dr. Doe brings up results of her imaging on the electronic health record. Ms. Jones is calm as all her medical records are secured with unbreakable quantum encryption. The quantum imaging software identified a 1/10 th mm square cluster of cancer cells near her original surgery site. Due to her history and after confirmation by the radiologist, Ms. Jones is scheduled for high precision radiation treatment the following week. A quantum computer is used to direct a radiation beam that destroys the cancer cells with extreme precision and spares all surrounding tissue. This rapid detection of a tiny cluster of cancer cells and their destruction is one of many potential advances in medicine that could occur pending the realization of quantum computing.

Many people are familiar with IBM's Watson computer which famously beat past Jeapordy! champions in 2011, or Google's AlphaZero machine learning chess algorithm that soundly defeated the best brute force computer chess champion in 2017. In 2018, IBM Watson Health now promises to provide more efficient patient care, population health, improved quality and earlier diagnoses, such as finding cancer at an early stage. 1 These ser vices are possible because Watson has been fed the American College of Physician Medical Knowledge Self-Assessment study guide and the Merk Manual of Diagnosis and Therapy. 2 The computational power to generate diagnoses in real time or to increase the precision of imaging to detect early stages of cancer may soon push beyond the limits of the standard binary 0/1 system available to Watson. Private industry (Google, IBM, etc.) and government agencies are already investing into quantum computing and artificial intelligence systems to make this happen. Thus, IBM may be one of the first to impact health care through quantum computing or artificial intelligence.

What is Quantum Computing and Why is it Different than Our Current Computer Technology?

Quantum computing is a revolutionary concept in digital data processing based on the fundamental principles by which nature operates, i.e. quantum mechanics. With advancements in physics in the early 20th century, methods of observation and purity of materials reached the level at which some quantum phenomena became detectable. The best example of this is a regular transistor present in ever y modern computer or device. It operates by directing large clouds of carriers of electrical current using engineered materials and quantum-based principles (band structure, localized states, etc.). They produce behavior unusual for naturally found materials - an ability to precisely control current with current, or current via light, or light via current.

Recently, miniaturization of transistors, detectors, and, at the same time, access to extremely low temperatures (such as −273C) have opened the possibility to operate, observe, and, most importantly, isolate individual particles with their inherently quantum properties. These properties are one reason the desktop or laptop computers we use have little resemblance to quantum computers (see ). Even though theoretically established for over 100 years, these properties have only recently been observed and verified over a wide range of particles and systems. While being nonsense in our daily experience, quantum mechanics has already led to dramatic advances in a few specific areas in medicine (e.g., MRI, laser surgery) with potential to revolutionize medical research and clinical care. 3 Because modern computers have largely saturated computational power and no longer grow exponentially as occurred in the last century, quantum computing, if realized, is the most promising technology for major advancements in processes currently beyond the reach of existing computing power. 4

All currently known and beneficial quantum algorithms that can run on quantum computers are based on the ability of the quantum system, upon certain rearrangement, to behave in unison. The system of many (quantum) particles, each holding a bit of information, behave as one - a property based on so-called quantum entanglement. For this reason, large chunks of data can be processed at once, operating essentially on only a few particles, that is, in a massively parallel manner. This will allow tasks 5- 7 that would require centuries of computing on a standard computer to require only a few minutes on a quantum computer.

A key challenge for quantum computers is to provide and maintain isolation of individual quantum particles (qubits, illustrated in ) involved in computation. Extreme and stable cooling is required to make wire circuits (or circuits made of other materials) behave in a quantum fashion. Cooling superconducting systems to about a 10 thousands of a degree above absolute zero creates the needed degree of isolation 8 to form computational quantum particles (qubits). Operated by electrical signal from a classical computer, these systems must be maintained at these extreme low temperatures by a large refrigeration apparatus involving a rare helium-3 isotope. In these extreme conditions, quantum particles (qubits) remain sufficiently isolated, or coherent, for about 0.1 ms - a 10 thousands of a second. 9 Some other systems based on natural and artificial atoms rather than metal wires have also reached similar isolation and operability. 10, 11

At this time quantum computing is an academic domain with users able to access and run programs in the cloud using 'IBM Q'. Yet larger and larger quantum computing devices have become commercially available 12 and can perform a small number of operations on a relatively small number of quantum particles (qubits). Most of these systems are based on superconducting technology and can contain up to about 100 qubits. Some quantum computational devices have even been made publically available for the research community to test and investigate small quantum codes via web interfaces (e.g., IBM Q). All currently available quantum computational devices, however, are far from a functioning quantum computer as they are not yet able to run large codes or work with 'big data'.

An important exception is one area of quantum computing - quantum cryptography. 13 With significant government investments (NSA, DOD, DOE), quantum cryptography is being already used to secure communications. 14- 16 Standard encryption methods rely on a keycode for the user to access encrypted data. However, the key must be shared and can be decoded by unauthorized persons seeking to 'hack' a system. With quantum computing, the key and the data can be secured indefinitely with guaranteed unbreakable encryption. Such strong security is possible because quantum encryption relies on the laws of nature (quantum mechanics) to furnish it. Thus, cryptography is expected to be the first application of quantum computing to enter medical practice to secure medical records and communication.

If Quantum Computing Becomes Available for Daily Use What Can We Expect to Change in Medical Research and Delivery of Care?

Big data research and machine learning is likely to be one of the fields to advance quickly with the advent of real world functional quantum computers. Beam and Kohane 17 provide an excellent description of machine learning, noting that there is a machine learning spectrum which runs from how much a predictive algorithm is driven by human decisions vs. decisions based on data. A statistical model requires human decisions about variable definitions and their inter-relationships. In machine learning, there are few assumptions and algorithms are derived by computer programs that evaluate millions of data elements and all their potential directions of effect and interactions. The more an algorithm is derived from raw data and with less human input, the more it fits into machine learning. Machine learning that informs clinical practice in real time depends on growing databases containing constantly updating medical record information and linked to other sources of data (e.g. wearable technology). To deal with this complexity future machine learning programs will require computational power of quantum computing to deliver results in real time. With more data informing predictive models and quantum computer power, the clinician may be able to select a therapy perfectly suited to an individual patient based on models continuously running and updated to predict treatment response while accounting for the numerous patient characteristics (race, age, gender, comorbidities, co-medications, genetic make-up, etc.) which contribute to outcomes.

With the first quantum computing devices becoming operational in around 2000, Parsons 18 speculated that quantum computing applications would generate a new era in medical imaging. It is expected that a quantum MRI machine will generate extremely precise imaging allowing visualization of single molecules. 3 Using artificial intelligence, quantum computing can be applied to interpreting diagnostic images. Not only will image detail be exponentially improved but the physician can be aided in interpreting results because efficient machine learning can train a quantum computer to identify abnormal findings with precision better than the human eye. 2

In cancer treatment, quantum computing will contribute to improved therapies. Computers are currently used to deal with the thousands of variables used to develop a radiation plan that targets cancer cells without damaging healthy ones. 19 Quantum computers would allow rapid and more precise radiation plans and allow comparisons between all possible approaches. The end result would be an ideal radiation dose pointed at the right target and thus lead to more effective treatment with reduced side effects. 19, 20

Machine learning and artificial intelligence have been used with traditional computing resources to interpret CT scans, aid surgical procedures and to analyze big data to develop predictive models of disease. Gulshan et. al. 21 used machine learning and nearly 10,000 retinal images to create an algorithm that can detect diabetic retinopathy with high sensitivity and specificity. The authors note that this is an experimental design with no known clinical utility. If programs such as the one created by Gulshan and colleagues 21 had the computational power of quantum computing, one could imagine running an algorithm in real time to help physicians accurately detect retinopathy, possibly at an earlier stage.

Machine learning holds great promise in eliminating most diagnostic errors related to reading imaging. Dilsizian and Siegel 2 describe how artificial intelligence can overcome the source of diagnostic errors in medical imaging. Computers overcome human sources of error such as: being stuck on initial impression, over-reliance on context, drawing quick conclusions based on a previous case and failure to consider other answers once a conclusion is made.

The volume of material and speed of new research discoveries is over whelming. Even the best attempts at providing guideline driven, evidence-based medicine are challenged by the rapid creation of new evidence. Combining big data with quantum computing will provide access to the current evidence and enable meaningful use of the electronic data continuously generated in the deliver y of care. Realization of personalized medicine will need to draw on analysis of mega-data and bring together measures of physiology, imaging, genomics, wearable technology, screening measures, patient records, environment measures and more. 20

Quantum computing holds no value to medicine without the parallel increase in availability of clinically meaningful data from numerous sources. Challenges to realizing a healthcare system where quantum computing uses decades of data from billions of clinical encounters and recommends a specific medication for a patient presenting with new onset depression, cancer, diabetes or any condition, will be overcome when data can be merged from multiple sources including existing patient data networks, bio-banks, and wearable health devices, etc.

Will the future include visits to virtual physicians? Most patients expect more than a pill when they seek care. Particularly when treatment involves greater risk as in surgery and when treatment effectiveness requires patient adherence to complex regimens. Quantum computing does not replace the doctor-patient relationship, transparency and trust required in shared decision making. Nor will a computer replace the ability of a physician to offer encouragement, empathy, social support and most importantly providing words of encouragement and a pat on the back, a handshake, a smile and even a hug. We must never underestimate the power of the social contract patients make with physicians and the support of doctors, nurses, staff play in healing. Computer algorithms, whether run on a "traditional computer" or a future quantum laptop computer, do not have feelings and will never be able to provide the impact of a surgeon's promise to "get you through this procedure" or the reinforcing effects of a primary care provider's encouragement to "walk just another 5 minutes" to enhance your health.

Summary

We may be at the dawn of a revolution in computing. At this time we can only point to experiments and speculate how quantum computing could improve imaging, diagnosis, treatment, and population health. It is yet to be determined if and when quantum computers will be accessible for daily use in medicine and research. We have numerous examples of machine learning algorithms and artificial intelligence that may leverage the power of quantum computing to deliver real time results. Until this level of access is available, quantum computing will remain the domain of researchers, not physicians.

Footnotes

Dmitry Solenov, PhD, (above), is in the Department of Physics, Saint Louis University, St. Louis, Missouri. Jay Brieler, MD, and Jeffrey F. Scherrer, PhD, are in the Department of Family and Community Medicine, Saint Louis University School of Medicine, St. Louis, Missouri.

Contact: ude.uls@vonelos.yrtimd
Disclosure
None reported.

References

3. Sullivan MD, Edlund MJ, Fan MY, Devries A, Brennan Braden J, Martin BC. Risks for possible and probable opioid misuse among recipients of chronic opioid therapy in commercial and medicaid insurance plans: The TROUP Study. Pain. 2010 Aug;150(2):332-339. [[ PMC free article] [ PubMed] Google Scholar]

4. N MA, C IL. Quantum Computation and Quantum Information. Cambridge: Cambridge University Press; 2010. [ Google Scholar]

5. LKG A fast quantum mechanical algoristhm for database search; STOC: ACM; 1996; p. 212. [ Google Scholar]

19. Pariente P. Quantum computing set to revolutionise the health sector. L'Aterlier BNP Paribas; 2015. [ Google Scholar]

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The Potential of Quantum Computing and Machine Learning to Advance Clinical Research and Change the Practice of Medicine

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Dmitry Solenov, PhD Jay Brieler, MD Jeffrey F. Scherrer, PhD We may be at the advent of a revolution in computer applications in clinical care and medical research. Quantum computing can exponentially advance computational power and promises to usher in a new epoch in computing technology.

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Das, R. (2016, October 12). Ten Top Technologies That Will Transform The Healthcare Industry. Retrieved May 24, 2019, from https://www.forbes.com/sites/reenitadas/2016/10/11/healthcare-2025-ten-top-technologies-that-will-transform-the-industry/#7b3ce2f03e18

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Nursing Informatics

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University of Maryland School of Nursing

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Nursing Informatics: Connecting Patient Care and Technology

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Using Virtual Reality to Train Physicians for Pediatric Emergencies

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Stanford Children's Health | Lucile Packard Children's Hospital Stanford

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General Anesthesia with Augmented Reality

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Abstract

INTRODUCTION

Clinician use of speech recognition (SR) technology for clinical documentation has increased in recent years. A recent survey reported that more than 90% of hospitals plan to expand their use of front-end SR systems (ie, direct dictation into free-text fields of the electronic health record [EHR]) in the coming years. As SR-assisted documentation has become more prevalent, there has been a simultaneous increase in research studying the effect of this technology on clinicians' workflow and medical practice. A comprehensive systematic review is needed to analyze and summarize relevant studies, identify knowledge gaps, and shed light on possible future research directions.

BACKGROUND AND SIGNIFICANCE

Several literature reviews about clinicians' SR use have been published over the past decade. A 2008 review of the use and evaluation of SR technology in hospital settings found that SR was most commonly used to assist in clinical documentation, although it was also used for interactive voice response systems, controlling medical equipment, and automatic translation systems. It also noted a lack of comprehensive, standardized methods for evaluating SR performance and utility, particularly those capable of considering the diverse clinical environments in which SR is used. This is a continuing problem, demonstrated by a 2015 review of SR in the radiology department, which found substantial heterogeneity across reviewed studies. Nevertheless, existing literature reveals a number of trends regarding the impact of SR on report turnaround time and accuracy. In a 2014 review of SR use in healthcare applications, of the 14 studies included, most evaluated productivity, which typically improved following SR adoption, and report accuracy, which was generally lower with SR than with other documentation methods. Hodgson and Coiera found a similar trend, with mean errors per report tending to be higher for reports created with SR compared with those created with traditional dictation and transcription.

Previous reviews included approximately 15-45 articles and often focused on a specific aspect of SR-assisted documentation, such as effect on productivity or accuracy.,, Although there is overlap in the time periods of this review and others, the present review includes over 120 articles from 10 scientific and medical literature databases relating to multiple aspects of SR-assisted clinical documentation over the past 3 decades. This broad scope reflects our aim to identify primary research questions pertaining to clinicians' use of SR for documentation, review major findings related to these questions, determine existing knowledge gaps and challenges, and ultimately propose specific areas we believe present significant opportunities for future research.

MATERIALS AND METHODS

Data sources and searches

This review was conducted in compliance with the 2009 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. We conducted systematic database searches to retrieve articles published from database inception through October 15, 2018. Databases searched include PubMed, the Cumulative Index to Nursing and Allied Health Literature, Web of Science, Association for Computing Machinery Digital Library, IEEE Xplore, ScienceDirect, MEDLINE, the Cochrane Database of Systematic Reviews, PsycINFO, and Scopus. We iteratively built and refined the search statements between April and June of 2017. We subsequently reviewed the references of included articles to identify articles missed by database searching. The final search statements and number of articles yielded are available in Supplementary Appendix A.

Inclusion and exclusion criteria

Inclusion in the review required that all of the following criteria be met: (1) the article was written in English; (2) the article included metadata (authors, title, publication year) and an abstract; (3) the article was published between January 1, 1990, and October 15, 2018 (SR was not widely used for clinical documentation until the late 1980s); and (4) the abstract mentioned speech or voice recognition, a medical setting, and use of SR for documentation or similar purposes (ie, it was not being used for therapy or interactive voice control systems). Prior literature reviews were excluded from analysis.

Article selection and annotation

Search results from each database were exported, and the title, author(s), journal title or conference name, and publication year were extracted. Duplicate articles were removed. A preliminary screening was conducted to exclude articles failing to meet all inclusion criteria.

Two authors (SB, JH) read the abstracts of all remaining articles, writing a brief summary of each and manually consolidating the summaries into a set of 19 research topics (Table 1).

Each article was then annotated by 2 reviewers with its research topic(s), medical domain, and SR system(s) evaluated, if applicable. Articles could be assigned up to 3 research topics. Disagreements between reviewers were resolved through discussion.

RESULTS

The article selection process is summarized in Figure 1. In total, of the 1343 records retrieved, 122 articles were included in the analysis ( Table 1).

Annotators agreed fully on medical domains and SR systems evaluated. They agreed fully on the research topics for 69 of 122 (56.6%) articles. Forty (32.8%) of the partial agreement cases involved 1 annotator selecting an additional research topic or missing a relevant topic. In the remaining 13 (10.7%) cases, the annotators selected completely different research topics.

Research trends over time

Most articles (89.3%) were published in or after 2000 (Figure 2A), with annual count fluctuating and peaking approximately every 7-8 years.

Most research topics, such as comparison to transcription, error analysis, SR use and impact on clinical workflows, and SR implementation, were studied throughout the review period. Since 2009, more studies have involved user surveys and interviews (Figure 2B).

Research topics

Documentation time/cost and productivity analysis

The most common research topic was documentation time or cost and productivity analysis, which applied to 48 (39.3%) articles ( Table 2). Most (27 [56.3%]) evaluated documents created in the radiology department.,,,,,,,,,,,,, The rest involved notes from a variety of medical domains, such as pathology (5 [10.4%]),,,, and emergency medicine (4 [8.3%]),,, Studies assessing productivity often did not list exact numbers of documents or speakers they evaluated; instead, many stated that all documents created during a certain time period were included the analysis, limiting the ability to compare and summarize results across studies. These studies also demonstrated substantial variation in how productivity was quantified, although certain measures, such as mean documentation time and turnaround time, did emerge as commonly used productivity indicators.

Mean dictation and/or correction time

Pediatrics

All reported increases after SR adoption, ranging from 13.9% to 200%, although 1 reported decreases if the SR error rate was ≤ 16%

Median, 80 th percentile, and/or 95 th percentile turnaround time

Radiology

The second most frequent productivity measure was report turnaround time, which was evaluated in 19 of 48 (39.6%) articles. All 19 found that implementing SR technology reduced mean and/or median turnaround times, often by more than 90%.,,,,,, Only 8 (16.7%) articles included cost analyses,,,,,, the most recent of which was published in 2008. Five (62.5%) of these studies reported decreases in documentation-associated costs following adoption of SR software.,,,, However, 2 (25.0%) reported increased costs, citing greater software expenses and the fact that highly paid physicians needed to spend more time editing their notes. The remaining paper proposed an econometric model for estimating the impact of SR and transcription on documentation-associated cost intended to help institutions decide which is most suitible.

Error analysis

Comparisons between, or assessments of the combination of, SR-assisted dictation and traditional dictation and transcription

Generally, studies comparing SR and transcription in terms of productivity found greater clinician productivity with SR than with traditional dictation and transcription (see Documentation Time/Cost and Productivity Analysis). However, studies comparing accuracy unanimously found more errors in self-edited SR-generated reports compared with those transcribed or edited by professional transcriptionists. For example, 1 study found that 23% of reports created with SR contained errors, compared with only 4% of those created with conventional dictation and transcription. Another found that 25.6% of SR reports contained errors, compared with 9.3% of those that were dictated and transcribed.

Impact on clinical workflow

SR methods

User surveys and interviews

SR implementation and other topics

DISCUSSION

We systematically reviewed articles retrieved from 10 scientific and medical literature databases spanning nearly 3 decades to assess the state of current research on the use of SR technology for clinical documentation and identify knowledge gaps and areas in need of further study. Overall, we found that existing research has focused largely on 3 topics: (1) the impact of SR on documentation time/cost and productivity, (2) the accuracy of SR-generated clinical documents and analysis of errors produced by SR systems, and (3) the relationship between SR and traditional dictation and transcription, including comparisons between the 2 documentation modes and analyses of how they can be used together.

In general, there has been a relative lack of studies conducted in nonradiology settings, although the magnitude of this inequity has lessened in recent years as SR use has become more widespread. However, assessing the accuracy and utility of SR on a large scale remains difficult due to continued inconsistencies in how these factors are evaluated. For example, although many studies reported differences in documentation time with SR compared with other input methods, some reported pre- and post-SR documentation times, while others only reported the actual time difference, making it difficult to compare time savings across studies. Similar heterogeneity exists in other commonly reported metrics, such as accuracy and cost.

Fewer articles involved SR methodology compared with other research topics, and most methodology articles were published before 2008. This may be because many hospitals now use SR systems provided by third-party vendors who manage and maintain the actual SR architecture (eg, language and acoustic models). Methods for automatically detecting errors in clinical documents created with SR technology have received particularly little attention. As SR-assisted documentation has become more prevalent, clinicians have expressed concerns about its accuracy and potential impact on document quality.

Clinical notes are a significant source of interprovider communication, and questions about the potential impact of SR technology on note accuracy, clarity, and completeness warrant careful study. Previous studies have shown that incorrect information in the EHR is a contributing factor in up to 20% of EHR-related malpractice cases and that copy-and-paste in particular contributes to 8%-10%., Among studies investigating how using SR to create notes affects the medical or linguistic quality of the document produced, results were mixed regarding whether SR technology was a help or a hindrance. Studies evaluating use of SR with templates or structured reporting were similarly mixed, suggesting that SR may not function well with current structured documentation methods.

Future directions

Based on the trends described previously, we have identified multiple aspects of SR-assisted documentation in need of further study, including, but not limited to, the following.

Impact on document quality and patient safety

Previous studies evaluating how SR affects documentation quality, particularly when used with structured reporting, indicate a need for additional research. Further investigation involving a broad range of input methods and documentation scenarios is needed to understand where and how SR can most appropriately and successfully be integrated with and used in the EHR. Studies have demonstrated that clinicians often underestimate errors generated by SR and do not have sufficient time to review their dictated documents. Education and training about SR-associated errors emphasizing the importance of manual revision and editing is needed, as is investigation into the effect of SR use on patient safety and outcomes. Differences in notes' linguistic quality may also impact the performance of downstream natural language processing tasks.

SR usability and clinical workflow

Choice of documentation method plays a key role in clinicians' satisfaction and their ability to perform their work efficiently and effectively. Therefore, studies focusing on understanding clinicians' practices, preferences, and potential concerns before, throughout, and after the SR implementation process remain necessary. Many well-studied and proven tools exist for measuring the usability of EHR and other software applications. Usability of EHR systems in general is a widely studied topic., However, only in recent years has usability of EHR systems integrated with SR software become its own area of study.,, SR technology allows physicians to dictate and edit their notes directly to the EHR without further assistance from traditional transcription or scribe services. While such a solution may reduce transcription costs, it may increase clerical burden to physicians already experiencing burnout. The development of robust and standardized scales, questionnaires, and other tools tailored for evaluating SR usability and clinical workflow may help identify specific problems and possible solutions.

Standardization of evaluation methods and metrics

Given the rising prevalence of SR in medical settings, not only for documentation but also in other aspects of health care and delivery (eg, voice-enabled care), the need for standardized methods and measures for evaluating its accuracy and effectiveness is greater than ever. Many of the reviewed studies offered imprecise or overlapping definitions of similar, but distinct, productivity measures. While some measures (eg, time to report availability) may directly impact patient care, others may impact clinician workflow (eg, dictation time) or reimbursement process (eg, turnaround time); as such, these measures may be worth studying and reporting independently. In addition, the phrase "error rate" has been applied at both word and document levels, and many studies only report 1 or the other of these metrics, despite the fact that both are useful measures of SR accuracy and should be reported. A recent review identified a similar pattern in studies about radiology report accuracy. Our findings suggest this trend is widespread, as it held true for articles related to emergency medicine, internal medicine, nursing, pathology, and more. This issue also exists in EHR usability analyses more broadly. For example, a 2017 review of literature related to EHR navigation found wide variation in the vocabulary used to discuss the same navigation actions and concepts.

The economic impact of SR adoption has also been inconsistently reported, despite increased financial pressures faced by many health care institutions. Therefore, systematic means of assessing the financial impact of different documentation methods, both prospectively (eg, by developing an econometric model) and retrospectively, are also needed.

Automatic error detection

Error detection systems intended for use with medical text have primarily been designed for written (typed) text., Typing errors frequently involve misspellings, while SR errors involve words which are spelled correctly (as an SR engine will only propose words that exist in its dictionary) but are incorrect given the context. Many studies included analyses of errors in documents created with SR; however, comparatively little work has been done toward developing automated methods of detecting and/or correcting these errors. While this may be partially due to researchers' lack of access to the inner workings of the "black box" of vendor SR systems, previously attempted post-SR error detection tools have shown promise in their ability to identify, and thereby ultimately reduce, errors. The development of automatic error detection methods for SR-generated medical text to improve document quality and patient safety therefore represents a significant research opportunity.

Limitations

Although we took steps to reduce the likelihood of having missed articles, including iteratively developing the search statements and screening the references of retrieved articles for additional papers, the database searches may not have yielded all relevant articles published during the time period of interest. Additionally, the included papers remain subject to reporting bias, and the heterogeneity of the included studies limited the ability to conduct a robust quantitative synthesis of their findings, even within individual research topics. Finally, the research topics we defined are subjective and based on the authors' prior knowledge and understanding of the field.

CONCLUSION

SR technology is increasingly used for clinical documentation. Research has been done to examine the effects of this technology on report accuracy and clinician productivity, largely focusing on a few clinical domains such as radiology and emergency medicine. However, a need remains for research to better understand SR usability when integrated with the EHR or other platforms, its impact on documentation quality, efficiency and cost, and user satisfaction over time and across different clinical settings. Standardized, comprehensive evaluation methods are also needed to help identify challenges and solutions for continued improvement.

FUNDING

This work was supported by Agency for Healthcare Research and Quality grant R01HS024264.

CONTRIBUTORS

The following authors contributed to the tasks of idea conception: LZ; development of database queries: SB, JH; development of annotation schema: SB, JH, LZ; article collection: SB, JH; article annotation: SB, JH, LW, ZK, LZ; development of analysis methods: SB, LZ; data analysis: SB; manuscript writing: SB, LZ; manuscript revision: SB, JH, LW, ZK, LZ.

ACKNOWLEDGMENTS

We thank Kenneth H. Lai, MA, Leigh T. Kowalski, MS, and Clay Riley, MA for their annotation work.

Conflict of interest statement: None declared.

REFERENCES

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Speech recognition for clinical documentation from 1990 to 2018: a systematic review

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Clinician use of speech recognition (SR) technology for clinical documentation has increased in recent years. A recent survey reported that more than 90% of hospitals plan to expand their use of front-end SR systems (ie, direct dictation into free-text fields of the electronic health record [EHR]) in the coming years.

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Stanford Children's Health | Lucile Packard Children's Hospital Stanford

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The Stanford Virtual Heart - Lucile Packard Children’s Hospital Stanford

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When talking about technology we see a lot of videos showing the good sides of new technology. What do you think are some of the downfalls to using technology?

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In regards to the future of nursing, how do you think one or all concepts: Quantum Computing,  Voice-Activated Communicators,  Game and Simulation Technology, Virtual Reality, and Mobile Devices will help with nursing practice or will not be of benefit to nursing practices?

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Blackley, S. V., Huynh, J., Wang, L., Korach, Z., & Zhou, L. (2019). Speech recognition for clinical documentation from 1990 to 2018: a systemic review. Journal of the American Medical Inforamtics Association, 324-338. https://doi.org/10.1093/jamia/ocy179

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Das, R. (2016, October 12). Ten Top Technologies That Will Transform The Healthcare Industry. Retrieved May 24, 2019, from https://www.forbes.com/sites/reenitadas/2016/10/11/healthcare-2025-ten-top-technologies-that-will-transform-the-industry/#7b3ce2f03e18

Solenov, D., Brieler, J., & Scherrer, J. F. (2018, September/October). The Potential of Quantum Computing and Machine Learning to Advance Clinical Research and Change the Practice of Medicine. Retrieved May 24, 2019, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6205278/

Das, R. (2016, October 12). Ten Top Technologies That Will Transform The Healthcare Industry. Retrieved May 24, 2019, from https://www.forbes.com/sites/reenitadas/2016/10/11/healthcare-2025-ten-top-technologies-that-will-transform-the-industry/#7b3ce2f03e18