Nurses Transforming Healthcare Through Informatics

Nursing Informatics Research Year in Review

Spring 2017 – Winter 2018

Trends in Clinical Informatics: A Nursing PerspectiveApril 27, 2017

Nurses Transforming Healthcare

Through Informatics

Andrew PhillipsPhD, RN

Nurses Transforming Healthcare Through Informatics3

Conflict of Interest

Andrew PhillipsHas no real or apparent conflicts of interest to report.

Nurses Transforming Healthcare Through Informatics4

Learning Objectives

• Evaluate themes that impact nursing informatics. • Identify gaps in nursing informatics research. • Generate logical next steps in advancing nursing

informatics research.

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Methods – Scoping Study

Arksey and O’Malley1

•Step 1 – Identify the Research Question •Step 2 – Identify Relevant Studies •Step 3 – Study Selection (Iterative process which can change over time )

•Step 4 – Charting the Data •Step 5 – Collating, summarizing, and reporting the results •Step 6 – Consultation – This is you guys

1Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International journal of social research methodology, 8(1), 19-32.

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Step 1: Research Question

•What trends and themes emerge from a survey of the published literature in the area of nursing informatics during the past year

•Make meaning of current and past themes – historical context.

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Step 2: Identify Relevant Studies

•Search Strategy−Databases: PubMed and CINAHL −Search terms

• ((“nurse” or “nursing”) AND “informatics”) OR “nursing informatics”

•Publication Dates 3/1/2017 – 2/28/2018

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Step 3: Study Selection

Inclusion and Exclusion Criteria•Inclusion criteria: Research, contributes to nursing informatics knowledge base, prototype development and testing, clinical care delivery focus; informatics

•Exclusions: Articles that focused on informatics education programs, nursing education, nursing students

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Search Results (flow chart)

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Step 4: Charting the Data

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21%

6%

27%

0%

33%

1%12%

Research by Setting (%)2017-2018

Ambulatory

Community/Public Health

Continuum of Care

Health Professional/Expert Panel

Hospital

LongTerm Care

Other (Theory/Design/Standards)

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3% 0% 3% 0%

58%

0%

16%20%20%

5%13%

48%

7% 7%6%12%

73%

3% 6%15% 16%

23%

4%

41%

1%8% 10%

16%11%

25%15% 15%

28%19%

0% 0%

51%

2% 1%

21%

6%

27%

0%

33%

1%

12%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Ambulatory

Community/Public

Health

Continuum of C

are

Health Profess

ional/Exp

ert Pan

el

Hospital

LongTerm Care

Other (Theory/

Design/Standard

s)

Not Applic

able

Research Setting (%)By Year

2012-2013 2013-2014 2014 - 2015 2015 - 2016 2016 - 2017 2017-2018 2018-2019

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49%

1%

3%

2%

5%1%

3%2%

17%

0

By Country (%)2017-2018

USA UK Korea Middle East Canada Tiawan China Australia Other

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0 0 0 0 0 0 0 0 0

62%

5%2%

9%

2%4%

65%

6%3% 3% 2%

14%

61%

8%

3%

7%

1%

13%

49%

1%3% 2%

5%

1%3% 2%

17%

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

USA UK Korea Middle East Canada Tiawan China Australia Other

Research Country (%)By Year

2014 - 2015 2015 - 2016 2016 - 2017 2017-2018 2018-2019

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5%

2%

7%

25%

0%

15%

3%

11%

24%

4%

4%

2%

7%

0% 5% 10% 15% 20% 25% 30%

Transitions

Standards/Terminologies/HIE

Technology Acceptance

TeleHealth/Mobile

Social Media

Implementation/Workflow

Human Factors

Patient Empowerment

Clinical Documentation/Documenetation

Big Data/Data Use

Public Health

Simulation

Other

Topic (%)2017-2018

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5%2%

7%

25%

0%15%

3%

11%

24%

4%

4%

2%7%

By Topic (%)

TransitionsStandards/Terminologies/HIETechnology AcceptanceTeleHealth/MobileSocial MediaImplementation/WorkflowHuman FactorsPatient EmpowermentClinical Documentation/CDSBig Data/Data Use

Public HealthSimulationOther

Telehealth/Mobile

Documentation/CDS

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0%

5%

10%

15%

20%

25%

30%

Transition

s

Standards/T

ermino

logies/H

IE

Technolo

gy Acc

eptanc

e

Technolo

gy Acc

eptanc

e-Pati

ent

TeleHea

lth/M

obile

Social M

edia

Implem

entat

ion/Work

flow

Human Facto

rs

Patient

Empowerm

ent

Clinica

l Docu

mentat

ion

Clinica

l Decis

ion Sup

port

Big Data/

Data Use

Public

Health

Hand O

ffs

Communicatio

n

Simulatio

n

Technolo

gy Dev

elopment

Other

Topic (%)by Year

2014 - 20152015 - 20162016 - 20172017 - 20182018 - 2019

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Step 5 – Collating, summarizing, and reporting the results

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Themes Identified

1. Clinical decision support (errors) and clinical documentation still main focus ~50% of studies.• Use of EHR data greater for

predictive tools – disease progression, deterioration

• New tools related to AI and machine learning, natural language processing

2. Patient Empowerment/Self-care• Disease management• Continuum of care (keep patients out

of hospital)3. Technology Acceptance by

Patient(Portals, Telehealth, Mobile)

3. Telehealth Standards

4. Social Media lacking from data (buried in telehealth?)

5. Use of telehealth to address health disparities and access

6. Patient Reported Data− Sustainability of patient facing

technologies7. Patient clearly recognized as

stakeholder8. Big Data Continues

• Data Mining and Use, AI, Machine learning

• Public and Population Health genomic, pharm, other

9. Inter-professional Care, Care Coordination, Shared Decision Making

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Representative Citations

Hewner, S., Sullivan, S. S., & Yu, G. (2018). Reducing Emergency Room Visits and In-Hospitalizations by Implementing Best Practice for Transitional Care Using Innovative Technology and Big Data. Worldviews on Evidence-Based Nursing, 15(3), 170-177.

Mosley, J. D., Feng, Q., Wells, Q. S., Van Driest, S. L., Shaffer, C. M., Edwards, T. L., ... & Thompson, W. (2018). A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers. Nature communications, 9(1), 3522.

Reading, M., Baik, D., Beauchemin, M., Hickey, K. T., & Merrill, J. A. (2018). Factors Influencing Sustained Engagement with ECG Self-Monitoring: Perspectives from Patients and Health Care Providers. Applied clinical informatics, 9(04), 772-781.

Reading, M. J., & Merrill, J. A. (2018). Converging and diverging needs between patients and providers who are collecting and using patient-generated health data: an integrative review. Journal of the American Medical Informatics Association, 25(6), 759-771.

Saleh, S., Alameddine, M., Farah, A., El Arnaout, N., Dimassi, H., Muntaner, C., & El Morr, C. (2018). eHealth as a facilitator of equitable access to primary healthcare: the case of caring for non-communicable diseases in rural and refugee settings in Lebanon. International journal of public health, 63(5), 577-588.

Topaz, M., Murga, L., Gaddis, K. M., McDonald, M. V., Bar-Bachar, O., Goldberg, Y., & Bowles, K. H. (2019). Mining fall-related information in clinical notes: Comparison of rule-based and novel word embedding-based machine learning approaches. Journal of biomedical informatics, 90, 103103.

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Nurses Transforming Healthcare Through Informatics

Step 6 – Consultation

Feedback and Professional Input…