What Can We Learn about Fall Risk and Prevention from EHR Nursing - - PowerPoint PPT Presentation

What Can We Learn about Fall Risk and Prevention from EHR Nursing Notes?

Ragnhildur (Raga) I. Bjarnadottir, MPH, PhD, RN Postdoctoral Associate, UF College of Nursing and Informatics Institute

Outline:

• Significance
• Theoretical framework
• Methods
• Results
• Discussion
• Implications

Significance

The problem of hospital falls

~1 million patient falls annually ~30-50% result in injury ~10% result in severe injury ~1% result in death

The Cost of Falls

Top 20 most expensive medical conditions

~$30,000 per fall

$34 billion in direct medical cost annually

Predicted to reach

$54.9 billion

by 2020

Evidence Gap

• Fall risk assessment tools have limited predictive value
• Lack of validation of fall risk prediction models
• Despite falls being considered a nurse-sensitive outcome, few studies

have examined registered nurses’ (RNs’) narrative notes as a source

• f actionable data

Purpose

To explore whether there is meaningful fall risk and prevention information in RNs’ electronic narrative notes not found in structured electronic health record (EHR) data.

Theoretical Frameworks

Donabedian’s Quality Framework

Structure Process Outcome

Choi’s Multi-Systemic Fall Prevention Model

Risk Factors Characteristics

• f

Interventions Outcomes

A Reduction in Falls and Fall Related Injuries Environment- Related Interventions

Extrinsic Risk Factors

Environmental Risk Factors

Care Process & Culture-Related Interventions

Intrinsic Risk Factors

Patient-Related Physiological and Psychological Factors

Technology-Related Interventions

Methods

Text mining:

• “The process or practice of examining large collections of

written resources in order to generate new information”

• A subset of data mining that focuses on unstructured data
• Natural Language Processing is “a component of text mining

that performs a special kind of linguistic analysis that essentially helps a machine “read” text”

Overview of process

Data Target Data Preprocessed Data Transformed Data Patterns Knowledge Selection Pre- Processing Trans- formation Data Mining Inter- pretation

• Medical Information Mart for Intensive Care (MIMIC-III) database
• Deidentified EHR data associated with patients who stayed in critical

care units between 2001 and 2012

Characteristics of full MIMIC III Sample (N=38,597) Age 65.8 (52.8-77.8) Male gender 27,983 (55.9%) Length of stay (median) 6.9 days (4.1-11.9) Hospital mortality 5,748 (11.5%)

• Supervised text mining using a lexicon developed based on:
• Review of literature, guidelines and terminologies
• Qualitative interviews with hospital nurses (Fehlberg et al, under review)
• Clinical and domain experts

Lexicon Develop- ment

Literature SNOMED- CT ICD-9 NANDA Clinical Experts Fall Prevention Toolkits

Results

Total l sam sample: 1,0 1,046,053 not

• tes fr

from 36 36,5 ,583 pati tients Risk Factors

Characteristics of Interventions

Outcomes

A Reduction in Falls and Fall Related Injuries

Environment-Related Interventions Extrinsic Risk Factors

Environmental Risk Factors

Care Process & Culture- Related Interventions Intrinsic Risk Factors

Patient-Related Physiological and Psychological Factors

Technology-Related Interventions Explicit mentions of fall risk or events

718 notes (0.07% of all notes) 406 patients (1.1% of all patients) 103,685 notes (9.9% of all notes) 23,025 patients (62.9% of all patients) 4,138 notes (0.4% of all notes) 1758 patients (4.8% of all patients) 2440 notes (0.23% of all notes) 1060 patients (2.9% of all patients) 130 notes (0.01% of all notes) 99 patients (0.27% of all patients) 414 notes (0.04% of all notes) 220 patients (0.60% of all patients)

Extrinsic Risk Factors: Physical Environment

• Fall circumstances
• “Fell on “a slippery floor” unwitnessed”
• Hazardous conditions
• “Found walking around the room using unstable furniture to aid him”
• Fall prevention activities
• “Kept bed low and locked; adequate lighting, items within reach”

Extrinsic Risk Factors: Staffing and Organizational Factors

• Understaffing
• “Transfer held until after 7p due to staffing issues on receiving unit”
• “Hospital ICU charge RN called during the night and advised that they have a

nursing staffing shortage for today”

• Adequate or additional staffing
• “Pt receiving 1:1 observation, RN staffing”
• Staffing through the care trajectory
• “The VNA would also need to know exact d'c date in order to have available

staffing”

Discussion

• Nurses narrative notes contain information about factors that

could impact fall risk

• Includes information not captured in routine structured data, such

as organizational and environmental factors

• Further research is needed to determine the predictive value of

these factors.

Limitations

• Dataset:
• Single hospital
• Critical care data only
• Unlabeled data (i.e. unable to distinguish between fallers and non-fallers)
• Methods
• Supervised approach limits findings to what is included in lexicon

Implications

• Findings highlight a potentially rich but understudied source of

actionable clinical and organizational data

• Application of novel methods to identify quality and safety measures

in RNs’ notes can uncover meaningful observations and clinical judgement for patient outcomes and health services research

• Future research should:
• Take into account clinical and contextual features (i.e., prevalence)
• Evaluate the predictive value of nursing assessment data found in progress

notes

Thank You!

Questions?

Contact:

Ragnhildur I. Bjarnadottir rib@ufl.edu Tel: 352-263-6508 1225 Center Drive, Suite 3214

Acknowledgements:

• Dr. Robert Lucero, co-author and Postdoctoral Mentor

Generously funded by UF College of Nursing and UF Informatics Institute

Additional slides

Fall-related ICD-9 codes

Code Description Notes External causes

• f accident

E884 Other fall from one level to another E885 Fall on same level from slipping, tripping or stumbling E887 Fracture, cause unspecified 34 E888 Other and unspecified fall 20 HAC Fall Codes (CMS) 800-829 Fractures 1 830-839 Dislocation 1 850-854 Intracranial injury 925-929 Crushing injury 940-949 Burn 15 991-994 Electric Shock 2 Total: 73

• Stop word removal:
• Removal of common words that don’t have meaning for analysis (e.g.

the, that, at, on)

• Stemming:
• Reduction of inflected words to their root (e.g. from falling to fall)

• A sequence of a certain number of words or characters from a larger

string

• Unigram = one word sequence (fall)
• Bigram = two word sequence (patient fall)
• Trigram = three word sequence (fall related injury)