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Virtual reality in use in operation room

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Austco has installed its latest IP-based nurse call system, Tacera, in a form that incorporates a speech recognition feature - obviating the need for patients to press a button when they need help - at Thames Hospice's new facility near Maidenhead.

Having served its local community for over 30 years, the hospice is reportedly the only adult such facility providing both inpatient and community palliative and end-of-life care services across East Berkshire and South Buckinghamshire. Its services are offered free to all who need them, 'thanks to the generosity of the local community and organisations'. It costs £12 m every year to keep the hospice running, and it relies on charitable support for over 50 per cent of the funds needed annually to continue providing its services.

Austco initially installed the standard Tacera nurse call system as part of the construction of the new hospice near Bray, but when key hospice staff visited the demonstration facility at its Bromsgrove headquarters, they saw the speech recognition and entertainment feature in action and felt it a 'must have' for their patients. Austco said: "The buttons / handsets etc. are still there, but the convenience, particularly for those patients that may not have the strength to press a button, enables nurses to be alerted should the patient need them. All patients have to do is call out 'Help, room XX', with the room number they are in, and a call is raised on the nurse call system. The entertainment system interprets the command, and sends a signal to the nurse call component, the two systems working together so the source of the command is translated into an alarm from the correct room. The call appears on the system as if the patient had pressed the call button. On entering the room, the nurse presses the presence button to show they are in attendance, and can cancel the alarm from there. The Austco system also offers patients all its usual entertainment options in their room."

Austco is currently working with Thames Hospice to see where else integrations and innovations can be used to improve even further on the levels of care offered.

See more about Austco

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Voice-activated nurse call alarm at Maidenhead hospice

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Austco has installed its latest IP-based nurse call system, Tacera, in a form that incorporates a speech recognition feature - obviating the need for patients to press a button when they need help - at Thames Hospice's new facility near Maidenhead.

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Abstract

The implementation of a mobile nursing information system (MNIS) has been shown to be beneficial for nurses, taking away the burden of paperwork and enabling them to focus more on patient care. However, the factors that contribute to a successful MNIS implementation have been not been widely studied. This cross-sectional study collected data based on the DeLone & McLean Information System Success Model (1992) from 177 registered nurses in Taiwan to evaluate the implementation of a MNIS. Results showed that user satisfaction, managers' support ( β = .223, P = 0.001), system quality (β = 382, P = 0.001) and service quality ( β = .161, P = 0.05) were significant predictor variables. The model consisting of the variables intention to use, managers' support ( β = .245, P < 0.001), years of RN experience ( β = .093, P < 0.05), user satisfaction ( β = .628, P < 0.001) produced an explained variance of 63.1%. An additional model consisting of system use and user satisfaction was also significant ( β = .493, P < 0.001) and contributed to 24.3% of the total variance. Identified factors that affect the use of the MNIS can help hospital administration develop strategies for more successful implementation of MNIS.

Introduction

The latest advancements in mobile technology and wireless communication have led to the development of nursing information systems (NISs) in some healthcare organizations. Mobile nursing information systems (MNIS) have been implemented in mobile devices such as laptops for years; they are used as part of both an electronic health record and a health information system (Gephart, Carrington, & Finley, 2015). Nurses use MNIS in various work environments to respond quickly in articulating their information needs. The benefits include the reduction of duplicate documentation and automatic data processing (such as automatic visualized vital signs, nursing diagnoses accompanied by disease diagnosis, and ultimately patient care outcomes) (Lee, Sun, Kou, & Yeh, 2017). However, some negative results have been reported by nurses (Gephartet al., 2015). How to maximize the use of MNIS to optimize patient care and satisfy nurses' increasing information needs is still a critical issue. Leveraging MNIS benefits requires further understanding of the practical issues facing the needs of an ever-changing society and the complexity of health care (Rouleau et al., 2017). This also requires knowledge on whether nursing leaders are able to take an active role in the implementation of MNIS to clinical practice.

The implementation of MNISs still has mixed results and some crucial challenges in some countries. An evaluation study of NISs reported that most nurses acknowledged NIS reduced documentation efforts (Sockolow, Rogers, Bowles, Hand, & George, 2014). But the researchers also identified several problems. First, most MNISs were mounted on nursing carts, which were usually placed in patient rooms or hallways; nurses were not comfortable that they had to stand several minutes during the use of MNIS cart. Second, MNISs are not as flexible as paper-based tools in sharing information during handover between nursing shifts (Sockolow et al., 2014), or in summarizing information on patient admission and discharge (Moghaddasi, Rabiei, Asadi, & Ostvan, 2017; Samadbeik, Shahrokhi, Saremian, Garavand, & Birjandi, 2017; Sockolow et al., 2014). Nurses' dissatisfaction with NIS is often found because the system does not fully support nursing duties and workflow (Ahmadian, Dorosti, Khajouei, & Gohari, 2017; Moghaddasi et al., 2017; Valizadeh & Bagheriyeh, 2018; Vollmer, Prokosch, Evans, & Kuttler, 2016). These problems of inefficiency and dissatisfaction impact nurses' willingness to use MNISs and may even hamper patient care.

Influencing factors of end users' perception are essential to successful system implementation. According to related technology acceptance models and system success theories, several key factors including organizational factors and task factors were shown to increase system use (Hsu & Wu, 2017; LaDage, Prasun, Linton, Kaiser, & Laskowski, 2015; Rashid, Abdulaziz, & Yasin, 2018; Valizadeh & Bagheriyeh, 2018; Zheng, Wang, Doll, Deng, & Williams, 2018). Others are individual willingness to use the technology and a fit between health IT and existing work practices (Vitari & Ologeanu-Taddei, 2018). These factors have not been comprehensively studied in the context of MNIS, which may lead to infrequent discussion in the literature. Moreover, previous studies on evaluation of MNISs is still limited. User acceptance of health information systems are continually studied (Shahzad, Jianqiu, Zia, Shaheen, & Sardar, 2018). User acceptance can be a prelude to or reflection of their satisfaction in using these systems, but acceptance is not representative of user satisfaction. A comprehensive method for gathering user satisfaction feedback is needed.

The Information System Success Model, known as the D&M IS Success Model, originated by DeLone and McLean in 1993, included dependent variables to measure the success and effectiveness of an information system. These variables are "information quality," "system quality" and "service quality" (DeLone & McLean, 1993). The framework was refined in 2003 and is commonly used in evaluating the success of information systems (Delone & McLean, 2003). There are some missing elements of successfully implementing MNIS. Hsu & Wu (2017) explored the insufficient level of supportiveness from administration in health organizations, the system not being specifically designed for nursing workflows, and the lack of training for system operation. Health care managers or nursing executives are responsible for the successful deployment of MNIS. As a result, the model does not fully interpret the situation or context of MNIS.

Although NIS have been widely studied in the past, there are continual updates of the knowledge about information technologies with potential application in health care. A plethora of studies (but by far not all) point out that there are relationships between external variables of health information systems based upon user acceptance and technologically related models (Kisekka & Giboney, 2018). Lin (2017) applied an integrated model to predict nurses' satisfaction with NIS by combining individual perception and technological functionality. But this research ignored organizational elements. Moreover, it is still unclear what the impact of administrative support is, whether positive or negative.

In the research context, this study assumed that a greater level of nursing leadership resulted in greater MNIS use. This study added elements into the success model to be more accurate and to address the effect of MNIS, including "managers' support." This research aims to explore more critical factors of the MNIS from the perspective of nurses.

Conceptual Framework

The adjusted D&M IS Success Model (Delone & McLean, 2003) was selected as the conceptual model for this study (Figure 1). This research suggests further investigation of the MNIS success model and defines the effectiveness of MNIS in hospitals. "Managers' support" was added as an input factor, resulting in having these inputs: "managers' support," "information quality," "system quality" and "service quality." Demographic variables were also added. By applying the D&M IS Success Model to this study, the four inputs may affect the "intention to use" MNIS. The "user satisfaction" may affect the "intention to use" of MNIS. Both "user satisfaction" and "intention to use" may affect the system use of MNIS.
Figure 1: Adjusted D&M IS Success Model
Systems quality measures technical success, referring to the extent to which systems are "user friendly" and can be used without any hassles. "System quality" is significantly related to "intention to use." Information quality measures semantic success, referring to the system's output value, as perceived by the respective user of the system. "Information quality" is significantly related to "intention to use." Service quality measures the combination of service provided rather than the product itself. It is defined as users' subjective assessment that the service they are receiving during systems testing and deployment is the service they expect. "Service quality" is significantly related with "intention to use." Use defines whether the behavior is voluntary or mandatory, informed or uninformed, effective or ineffective. One of the major success dimensions of IS success is user satisfaction, which measures the users' level of satisfaction with a system.

Furthermore, the relationship between the demographic information of staff and the factors in the model were analyzed to investigate whether the characteristics of staff have an effect on their opinions of MNIS.

Methods

Sampling and Setting

This research adopted a cross-sectional design based on purposive sampling techniques. The research was conducted at two Taiwanese hospitals, which adopted computerized nursing diagnoses in 1999 and an NIS in 2008. The target population consisted of nursing staff at each hospital. Out of the 10 nursing units that were using MNIS, eight were selected. Staff with the experience of using MNIS over three months were considered as potential candidates. The inclusion criteria were nursing staff who (a) were more than 20 years old, (b) worked more than three months and not part-time, (c) were able to read Chinese, and (d) were willing to participate in research and sign consent forms. Exclusion criteria were (a) an inability to answer questionnaires clearly; (b) working less than 3 months or employed part-time. A total of 225 registered nurses were invited to participate in the study.
The functionality of MNIS
The function of the MNIS included (1) recording nursing information, (2) nursing plan, (3) diagnosis related to nursing decision, (4) patient identification before procedure, (5) patient education, (6) attendance performance, and (7) official duties such as diet orders and adverse event reporting.

Measurement

The survey included demographic items and the IS Success Model scale. The demographic items included age, education, RN years, and RN level. The levels of registered nurse ranging from 0 to 4th level were assessed by performance appraisal in accordance with procedures agreed to by the Taiwan Nursing Association and the employer. The items of the constructs adapted from DeLone & McLean model consisted of seven dimensions, 43 items, graded by a five-point Likert scale that ranged from strongly agree (5) to strongly disagree (1). The validity and reliability of the tool were validated in previous research. The Cronbach's alpha was reported to be 0.78 - 0.83 (Ahmadian, et al., 2017).

Process

The researchers introduced the study to each of the eight nursing units, including the purpose of the study, the method, the time required to complete the questionnaire, and how the data would be used after they were collected. A questionnaire survey was conducted at two hospitals in Taiwan that implement the same nursing information systems and devices. The participants filled out the questionnaire and returned it within five days.

Ethical Considerations

The study was conducted after the Institutional Review Board approval (reference number CGH- 98-0415B). Written informed consent forms were obtained from all participants. Participants were given instructions about the study purpose and the questionnaires. They were informed that participation in this study was voluntary, and that they could refuse or withdraw from the study at any point without giving any reason. Moreover, the participants were reassured that their responses would be kept confidential and their identities would not be revealed on research reports and publications of the study.

Data Analysis

The researchers used the IBM SPSS software to analyze the statistical results. Multiple regression analysis was used to examine model fit for each construct and its subconstructs (to assess the measurement model).

Results

The 225 nurses who fulfilled the inclusion criteria comprised the sample of the study, of which 216 completed the survey, 39 were excluded due to discontinued use of the MNIS, yielding a final response rate of 78.67% (N = 177). Table 1 details the demographic information of the respondents. The participants were all female, 83 (46.9%) of them were 26-30 years old, the average age was 28.57 (SD = 4.67) years, 96 (54.2%) had nursing experience for less than five years. Most of the participants had an associate (86, 48.6%) or bachelor's degree (87, 49.2%), 78 (44.1%) were at the N2 level, meaning a registered nurse who has demonstrated competence in basic nursing practice and worked at least 2 years.
Table 1: Participant Demographics
Cronbach's alpha for total scores was 0.889, and for constructs of MNIS success, the model ranged from 0.872 to 0.966 (Table 2). The descriptive statistics for these constructs indicated that most rated above the median score, managers' support (mean = 28.76, SD = 5.24), system quality (mean = 21.09, SD = 5.20), information quality (mean = 13.61, SD = 2.96), service quality (mean = 9.18, SD = 2.7=56), intention to use (mean = 13.98, SD = 3.18), and user satisfaction (mean = 26.03, SD = 6.09).
Table 2: Cronbach's alpha for total scores
Weak to strong correlations were found in the measures of the MNIS success (Table 3). There were positive correlations of quality-related constructs (system, r =.596, p < .01; information, r =.535, p < .01; service, r =.542, p < .01), and managers' support ( r =.712, p < .01) with intention to use. The quality related constructs also have positive correlations (system, r =.694, p < .01; information, r =.529, p < .01; service, r =.574, p < .01), and managers' support ( r =.588, p < .01) with user satisfaction. The correlation between system use and other constructs of MNIS were also weak to moderate.
Table 3: Correlations for all variables
Table 4 demonstrates the association of demographic and work characteristics with the constructs of the MNIS success model. As nurses' experience were over 10 years, they were significantly more satisfied with the MINIS (F =4.988, P <.01). There were significant differences in "user satisfaction" and "intention to use" among four groups of RN level 0~4.
Table 4: Nursing characteristics and constructs of the MNIS success model
Multiple regression models were built to identify variables independently related to user satisfaction, intention to use and system Use (Table 5). First, the model of predicting for user satisfaction, managers' support ( β = .223, P < 0.001), system quality ( β = 382, P < 0.001) and service quality ( β = .161, P < 0.05) had a stronger impact on user emotion.
Table 5: M Figure 2: Parameters of the MNIS success model ultiple regression results for each variable
The explained variance of user satisfaction model by each predictor is 56.5%. Second, the model of intention to use, managers' support ( β = .245, P < 0.001), years of RN experience ( β = .093, P < 0.05), user satisfaction ( β = .628, P < 0.001) was analyzed. The explained variance of intention to use model by each predictor is 63.1%. Third, the model of system use, user satisfaction was significant ( β = .493, P < 0.001) and contributed to 24.3% of the total variance. The parameters of the MNIS success model were illustrated in Figure 2.

Discussion

This study analyzed the factors of MNIS implementation according to the DeLone and McLean theory (1993, 2003) to provide an insight on how to successfully implement a mobile NIS by measuring users' perceptions. The study found that the domain relationship of RN years and managers' support for the use of the MNIS were positively related. The most powerful predictor about user satisfaction was system quality. Furthermore, user satisfaction is also the most powerful predictor in both behavioral intention and actual use of the MNIS. Managers' support had a significant impact on the implementation of MNIS and nurses' satisfaction.

According to the result, most of the staff continued to use MNIS over three months and showed a positive attitude toward it. This shows that most of the staff were positive about the system. The characteristics of the staff, including age and service experiences, failed to show significant difference toward the factors of the model; this complies with past research that the attitudes of nursing staff would not be affected by personal factors such as age or service experience (Lin, 2017). A well-designed information system is desirable for nurses despite their individual differences (Gephart, et al., 2015).

It appears that the increase of intention to use MNIS leads to higher user satisfaction (Rashid, Abdulaziz, & Yasin, 2018). To increase the intention of using MNIS, an emphasis on manager support and system quality should be included. Previous research confirmed that manager support is crucial in adopting new technology, including providing enough training, clearly introducing the purpose and aim of the adoption, and keeping a close follow-up watch for negative effects of the adoption.

It is worth noticing that information quality does not necessarily affect the intention or the consistency of using MNIS. This may factor on whether MNIS could provide correct, comprehensive and timely information and whether the screen size fulfills the needs of work tasks but does not necessarily affect the staff using MNIS.

Limitations

Technology adoption affects the work of all the staff in the hospital. A limitation of this study is that it only recruits the opinions of nurses. Also, the data were collected from two medical institutes to ensure that the model does not merely represent a single phenomenon or setting. However, the two institutes were still established under the same foundation thus the generality of the model still needs to be validated. Moreover, this study only sampled eight units across these two hospitals. More data could be retrieved to reveal further insights into new technology adoption. Advanced MNIS design also requires more knowledge that can contribute to improving efficiency and satisfaction solutions in the era of healthcare system reform (Andargoli, Scheepers, Rajendran, & Sohal, 2017).

Conclusion
The views and opinions expressed in this blog or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.
The results of this study established the variable "managers' support" linking the use of MNIS, adding to our understanding of nursing management, which is critical in the context of MNIS implementation. Although DeLone and McLean's theory (1993) provides a useful structured view to assess the successful factors of information systems, more nursing-specific elements should be considered for nurses as real end-users. Thus, a holistic viewpoint can better guide nursing informatics research.

The use of technology has been held as a symbol of a modern organization, and patients, nurses and health care managers can all benefit from the lessons learned during the evaluation of nursing information systems. This research considers the working environment of health organizations and proposed a model to facilitate the feasible evaluation of technology implementation in the future. There has been and will be in the future a need for updating MNIS to meet the changing requirements of the current health care system to serve both patients and nurses. A success model can help healthcare organizations determine specific information system dimensions and characteristics in need of improvement.
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Citation: Cheng, C.C., Chan, C.L., Chen. L. & Guo, S.H.M. (Fall, 2019). Evaluation of the implementation of a mobile nursing information system. Online Journal of Nursing Informatics (OJNI), 23 (3), Available at http://www.himss.org/ojni (OJNI), 21(2).

Online Journal of Nursing Informatics

Powered by the HIMSS Foundation and the HIMSS Nursing Informatics Community, the Online Journal of Nursing Informatics is a free, international, peer reviewed publication that is published three times a year and supports all functional areas of nursing informatics.

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Evaluation of the Implementation of a Mobile Nursing Information System

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This study in the Online Journal of Nursing Informatics looks into the implementation of mobile nursing information systems that have been shown to be beneficial for nurses, taking away the burden of paperwork and enabling them to focus more on patient care.

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Voice Technology Can Improve Healthcare Engagement

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In 2015, Saratoga Hospital in Saratoga Springs, New York, unveiled a new, state-of-the-art intensive care unit that, at 20,000 square feet, is more than triple the size of the previous ICU.
THE PROBLEM
All rooms are private, enhancing the patient experience and reducing the risk of spreading infection. At the same time, caring for patients in a much larger space presented a communications challenge. Saratoga needed a solution that would enable staff to quickly and safely share information and request support, when needed.
PROPOSAL
During the planning stages, leadership at Saratoga Hospital realized that they would need an enterprise clinical communication solution to connect team members and maintain the patient surveillance and safety that are essential to delivering high-quality care.

"Given the high-risk environment of the ICU, our nurses wanted a system that would work seamlessly under isolation gowns and allow team members to summon help quickly," said Diane Bartos, RN, director of critical care at Saratoga Hospital. "After assessing several systems, we selected the Vocera Badge, a wearable, hands-free, voice-controlled device that streamlines communication."

"If a patient needed to be put on a ventilator, only the intubation team entered the room. The charge nurse stood outside, observing the procedure and communicating via the badge to guide the team as needed and help prevent cross-contamination."
Diane Bartos, RN, Saratoga Hospital

There's no need to remember phone numbers or who is on call; instead, clinicians simply say the name, role, or group of the person or team they want to call, and they are connected, she explained.
MARKETPLACE
On the clinical communications technology front, vendors include Avaya, Halo, HipLink Software, Mobile Heartbeat, PatientSafe Solutions, PerfectServe, Spok, Telmediq and Vocera.
MEETING THE CHALLENGE
Shortly after deploying the communication badges in the ICU, Saratoga rolled the technology out across the hospital. Team members who often need to be hands-free - nurses, respiratory therapists, security personnel, EKG technicians and others - wear the badges.

The badges are integrated with the organization's Rauland Nurse Call system and Philips Patient Monitors, enabling patient requests and notifications to go directly to the assigned nurse or care team. Physicians primarily use the secure Vocera smartphone app to communicate and collaborate.

"Before the first wave of COVID-19 hit Saratoga Hospital, staff already were equipped with a hands-free communication solution that can be worn and used under PPE," Bartos said. "As part of our pandemic preparation, we focused on educating frontline staff on the proper way to put on and remove PPE to avoid self-contamination. The Vocera Badge played a significant role in these education efforts."

During the safety training, which took place inside and outside an anteroom, the educator would observe and guide team members as they donned and doffed their protective equipment. The educator and staff members wore the badges to communicate during the training, a practice that helped nurses, doctors, respiratory therapists and other care team members reduce the risk of self-contamination.

"At the peak of the pandemic, care teams in the ICU used hands-free communication to reduce staff exposure and improve safety by keeping the number of team members in a patient room to a minimum," Bartos recalled. "For example, if a patient needed to be put on a ventilator, only the intubation team entered the room. The charge nurse stood outside, observing the procedure and communicating via the badge to guide the team as needed and help prevent cross-contamination."

Without the communication badges, Saratoga would not have been able to limit the number of clinicians in the isolation room and help nurses, doctors, respiratory therapists and other care team members stay safe, she said. Staff members who intubate COVID-19-positive patients say they take comfort in having that extra set of eyes focused on identifying and preventing opportunities for cross-contamination, she added.
RESULTS
The communication badges have improved care team communication and safety, saved time, and helped conserve valuable PPE resources, Bartos said.

"One of our COVID-19-related adaptations is to appoint staff members as runners who bring supplies to team members working in isolation rooms," she explained. "Clinicians and runners all wear the badges. If a clinician dons PPE, goes into a patient room, and suddenly realizes he or she forgot something, the clinician can easily communicate with a runner."

The runner then quickly gathers and delivers the forgotten item. This process minimizes the number of times a clinician must leave and re-enter a room, and in turn, don and doff PPE. That saves time and precious resources.
ADVICE FOR OTHERS
"In emergency situations and isolation environments, care teams need hands-free, voice-controlled communication solutions that can be worn under PPE," Bartos contended. "This technology enables immediate consultation, helps protect patients and providers, and facilitates hands-on care. In situations with increased risk for contamination, hands-free solutions offer significant advantages over smartphones and other options."
Twitter: @SiwickiHealthIT
Email the writer:bsiwicki@himss.org
Healthcare IT News is a HIMSS Media publication.

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Hands-free voice tech helps Saratoga Hospital save PPE and ensure staff safety

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In 2015, Saratoga Hospital in Saratoga Springs, New York, unveiled a new, state-of-the-art intensive care unit that, at 20,000 square feet, is more than triple the size of the previous ICU. THE PROBLEM All rooms are private, enhancing the patient experience and reducing the risk of spreading infection.

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1) In your nursing experience, what type of mobile device do you prefer to use to provide patient care & why, such as; C.O.W., laptop, iPad, etc.?

2) Healthcare 2021  Trends:

Health informatics is constantly evolving, please read through the 5 major trends taking shape in 2021 found on https://healthmanagement.org/c/hospital/news/healthcare-2021-five-major-trends

Once you have read through the trends please choose a trend you were surprised to see or expected to see on the list, why do you feel this trend will be important for the future of health informatics?

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Technology and informatics-related processes and activities continue to evolve with each passing year. As we finish 2019 and move into 2020, a few trends in this evolution come to mind - ones that could serve healthcare and nursing well. Here are my predicted top trends for 2020.

SOURCE: Kaminski, J. (2019). Informatics Trends to watch in 2020. CJNI: Canadian Journal of Nursing Informatics, 14(4). Retrieved from http://cjni.net/journal/?p=6615

Speech Recognition Software

Several experts including Dr. Eric Topol have advocated for speech-recognition software to address the burn-out and time consuming need to document through typing into electronic health systems. Although voice-recognition software has been available for decades, the precision that is needed to be useful to healthcare has rarely been realized until recently. Even now, available software requires more sophistication to be seamless and adaptable to health provider needs. Dr. Topol expanded on why voice recognition software could be a real boon for healthcare communication and documentation through "...keyboard liberation or using natural language processing of speech to synthesize notes and eliminate the ultimate source of distraction and dislike in medical encounters" (O'Connor, 2019, p.1). This is a trend to watch for in 2020, one that could make nurses' work life much easier and less time-consuming.

View the Video: The Benefits of Speech Recognition - G2 Speech (1:26 minutes)

G2 Speech. (2019, June 6). The Benefits of Speech Recognition - G2 Speech. Video File. Retrieved from https://youtu.be/GdA6g1gHOcc

As well, speech-recognition software can act as an assistant to people in general. For example, Amazon Alexa and Leela can interface with clients to help them to set appointments, receive results, and record readings. This virtual assistant approach can also be used within healthcare facilities to enhance access to information and support. This is another trend to look out for this year. View this brief video to see examples of these systems in action within people's homes:

Voice technology for the healthcare industry (0.32 minutes)

Conduent. (2019, February 6). Voice technology for the healthcare industry. Video File. Retrieved from https://youtu.be/P23zYSWlAWQ

Personalized Healthcare

Machine learning, artificial intelligence, wearable and portable biosensors will contribute to the rise of true personalized medicine and healthcare. This trend includes personalized or precision medicine, defined as "...the tailoring of medical treatment to the individual characteristics of each patient. The approach relies on scientific breakthroughs in our understanding of how a person's unique molecular and genetic profile makes them susceptible to certain diseases. This same research is increasing our ability to predict which medical treatments will be safe and effective for each patient, and which ones will not be" (Personalized Medicine Coalition, n.d., p. 1).

Personalized healthcare also has a more expansive meaning, that incorporates client preferences, choices, and input to truly tailor their own healthcare. "An array of new technology advancements, including 3-D printing, robotics, nanotechnology, genetic coding, and therapeutic options can permit more personalized and accessible patient care. Many devices and equipment are getting smaller and more portable, and treatments will likely become more targeted-all of which can make future health care more mobile and precise. This, in turn, should increase staff and process efficacy and improve patient outcomes, as clinicians will be able to quickly find the best treatment option rather than try multiple interventions. Personalization of medications, for instance, will be based on a patient's genetic profile and the use of precision medicine, whereas designs for 3-D-printed prostheses will be based largely on a patient's specific anatomy" (Deloitte Development, 2017, p. 6).

Many people use health apps that they can download from the Apple or Android Stores to monitor their own health particulars, track health behaviors or data progressions such as weight loss, or fertility related data. People also often use wearable devices such as fitness trackers to monitor and track their physical activity which may include GPS related data such as location, and routes used for running and walking. Other apps may be used to monitor more physiological data such as pacemaker activity, heart rate, blood pressure, and so on. All in all, apps must meet standards to be recommended to clients. Health professionals can suggest which apps are the best choices for keeping their data secure, affording the best experience for the client through expert design, and API connected so that data can be shared with their health providers if they so choose.

Mobile apps used within practice need stringent measures built in to ensure personal health information (PHI) protection using APIs, encryption, and data capture solutions. Using mobile and wearable apps can significantly boost communication and understanding between health professionals and patients. When apps are designed according to national standards, they can be very efficient modes for sharing data on a regular basis that can support goals, tracking to gauge progress, and keep an eye on conditions that require monitoring which all contribute to personalized healthcare.

View this brief video to see examples of these systems in action:

Dr Eric Topol: Preparing the healthcare workforce to deliver the digital future (3:57 minutes)

Health Education England. (2018, June 26). Dr Eric Topol: Preparing the healthcare workforce to deliver the digital future.Video File. Retrieved from https://youtu.be/f2UTxtgSFzU

Predictive, Prescriptive and Cognitive Analytics

As technology continues to evolve, exciting new abilities are emerging which show great potential in supporting personalized, value-based care. Technologies such as artificial intelligence (AI), predictive analytics and cognitive computing all show great promise in helping health professionals make valuable predictions of trends, diagnoses and outcomes. "AI is the field of study that attempts to replicate human abilities, but without human limitations of time, energy and power. By using advanced algorithms, data processing capabilities and IT systems can produce data driven predictions within seconds - with little to no human intervention. Predictive analytics backed by real time and historic data processing can identify risky medical conditions ahead of time.

Predictive analytics uses technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients. In medicine, predictions can range from responses to medications, to hospital readmission rates. Examples include predicting infections from methods of suturing, determining the likelihood of disease, helping a physician with a diagnosis, and even calculating future wellness" (Patil, 2019, p.1).

Innovators in healthcare technology are working on ways to seamlessly incorporate predictive analytics and AI into electronic health records (EHR) using deep and machine learning coupled with natural language processing techniques to move more towards personalized healthcare. Predicting outcomes from historical data is nothing new in healthcare - health professionals have used it for years. For instance if people engage in certain behaviors such as smoking, eating a high-fat, high-sugar diet, living sedentary lives, and so on, health care professionals can predict likely outcomes. However with predictive analytics, health professionals can tie together all sorts of protected health information (PHI) or big data to personalize predictions beyond common generalizations such as smoking leads to lung cancer or chronic lung conditions in many people. But it does not stop there: predictive analytics is just the beginning of imminent powerful analytics in healthcare. Analytic types currently include descriptive, diagnostic, predictive, prescriptive, and cognitive analytics (Figure 1).

Figure 1: Descriptive, diagnostic, predictive, prescriptive, and cognitive analytics.

Predictive analytics is only one type of analysis that can be used in healthcare. An essential dichotomy of analysis methods are now within reach - each level provides a unique perspective of health data and degree of sophistication. Descriptive and diagnostic analysis have been used in healthcare for decades, but predictive is just emerging, and prescriptive and cognitive analysis are still very rare.

Prescriptive analytics uses optimized algorithms, modeling, rules and decision techniques to build on predictive results and propose actions to be taken. "Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made, it tries to provide how to do the actions and what are the consequences of these actions to derive a better decision. Prescriptive Analytics can be used anytime you need to advise users on what action to take" (Sami, 2017, p. 1).

"Cognitive analytics combines a number of intelligent technologies like artificial intelligence, machine-learning algorithms, deep learning etc. to apply human brain-like intelligence to perform certain tasks. Basically, this type of analytics is inspired by how the human brain processes information, draws conclusions and codifies instincts and experience into learning such as understanding not only the words in a text but the full context of what is being written or spoken. All these intelligent technologies make a cognitive application smarter and more effective over time by learning from its interactions with data and with humans" (WeirdGeek, 2018, p. 1).

Together, predictive, prescriptive, and cognitive analytics can transform healthcare in astounding ways.

Although predictive analytics offers amazing insights that can support patient outcomes, personalizing healthcare also requires prescriptive and cognitive analytic capabilities coupled with stringent security features. Systems like IBM Watson can help health professionals support their clients in personalized ways that customize their treatments, client education, and healthcare decisions.

Watch the following video, for greater understanding of how predictive, prescriptive and cognitive analysis and AI can be applied to personalize healthcare for individual clients.

How It Works: IBM Watson Health (4:03 minutes)

IBM Think Academy. (2015, May 20). How It Works: IBM Watson Health. [Video File]. Retrieved from https://youtu.be/ZPXCF5e1_HI

3D Printing

3D printing is one of the most amazing and practical technologies that is rising in healthcare. "3D printing has come a long way since its debut, especially in its uses in the healthcare industry. The technology offers faster prototypes, creating everything from personalized prosthetics to "poly-pills" at a fraction of the cost. The customizable aspect of 3D printing is revolutionizing organ transplants and tissue repair, and it's even able to produce realistic skin for burn victims" (Ghosh, 2019, p. 1).

This astonishing breakthrough presents mega-opportunities to health care since these printers can be tailored to use anything from plastics to stem cells as their medium. Artificial bones can be constructed that are accepted by the body and work well after surgical reconstruction. Prosthetics can be customized more accurately for amputees, and poly-pills can be created that combine several drugs at once for people with multiple illnesses.

View the following videos to get an idea of how 3D Printing can revolutionize healthcare.

The Ultimate List of What We Can 3D Print in Healthcare - The Medical Futurist (2:58 minutes)

The Medical Futurist. (2018, May 14). The Ultimate List of What We Can 3D Print in Healthcare - The Medical Futurist.Video File. Retrieved from https://youtu.be/9XYLRaVqzNY

3D printing for biomedical applications (2:54 minutes)

Research Square. (2019, August 21). 3D printing for biomedical applications. Video File. Retrieved from https://youtu.be/vk6ALcQiZ2w

Although there are many other technological breakthroughs coming down the pipeline of innovation, these trends of Speech Recognition Software, Personalized Healthcare, Predictive, Prescriptive and Cognitive Analytics, and 3D Printing are far-reaching and utilize many of these other innovations in their processes. Keep your eyes open to see how these predictions play out over the next year, and how they affect or could enhance your professional practice.

References

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Deloitte Development. (2017). The hospital of the future: How digital technologies can change hospitals globally.Retrieved from https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/us-lshc-hospital-of-the-future.pdf

G2 Speech. (2019, June 6). The Benefits of Speech Recognition - G2 Speech. Video File. Retrieved from https://youtu.be/GdA6g1gHOcc

Ghosh, I. (2019). 5 Ways Technology is Transforming the Healthcare Industry. Visual Capitalist, February 27.Retrieved from https://www.visualcapitalist.com/5-ways-technology-healthcare-industry/

Health Education England. (2018, June 26). Dr Eric Topol: Preparing the healthcare workforce to deliver the digital future.Video File. Retrieved from https://youtu.be/f2UTxtgSFzU

IBM Think Academy. (2015, May 20). How It Works: IBM Watson Health. [Video File]. Retrieved from https://youtu.be/ZPXCF5e1_HI

Intel AI. (2018). Predictive analytics in health care. White Paper. Santa Clara, CA: Intel Corporation. Retrieved from https://www.intel.ai/nervana/wp-content/uploads/sites/53/2018/05/Predictive-Analytics-in-Healthcare.pdf

O'Connor, A. (2019, March 11). How Artificial Intelligence Could Transform Medicine. The New York Times. Retrieved from https://www.nytimes.com/2019/03/11/well/live/how-artificial-intelligence-could-transform-medicine.html

Patil, A. (2019, Jan. 15). AI and predictive analytics lead to improved delivery of healthcare services. Healthcare Business & Technology. Retrieved from https://www.healthcarebusinesstech.com/ai-and-predictive-analytics-lead-to-improved-delivery-of-healthcare-services/

Personalized Medicine Coalition. (n.d.). The Age of Personalized Medicine. Retrieved from http://www.personalizedmedicinecoalition.org/Userfiles/PMC-Corporate/file/pmc_age_of_pmc_factsheet.pdf

Research Square. (2019, August 21). 3D printing for biomedical applications. Video File. Retrieved from https://youtu.be/vk6ALcQiZ2w

Sami, M. (2017). The Evolution of Analytics | The 5 Types of Analytics. Retrieved from https://melsatar.blog/2017/07/30/the-evolution-of-analytics/

The Medical Futurist. (2018, May 14). The Ultimate List of What We Can 3D Print in Healthcare - The Medical Futurist.Video File. Retrieved from https://youtu.be/9XYLRaVqzNY

WeirdGeek. (2018). 5 Types of analytics: Prescriptive, Predictive, Diagnostic, Descriptive and Cognitive Analytics.Retrieved from https://www.weirdgeek.com/2018/11/types-of-analytics/

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Technology and informatics-related processes and activities continue to evolve with each passing year. As we finish 2019 and move into 2020, a few trends in this evolution come to mind - ones that could serve healthcare and nursing well. Here are my predicted top trends for 2020. SOURCE: Kaminski, J.

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Dr Eric Topol: Preparing the healthcare workforce to deliver the digital future (short version)

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Dr Eric Topol: Preparing the healthcare workforce to deliver the digital future (short version). (2018, June 26). [Video]. YouTube. https://www.youtube.com/watch?v=f2UTxtgSFzU

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