A Case Control Study of Pediatric Falls Using EMR

Transcription

1 A Case Control Study of Pediatric Falls Using EMR Patricia R. Messmer, PhD, RN-BC, FAAN Consultant- Nursing Education & Research Broward Health- Broward General Medical Center Chris Evert Children s Hospital, Fort Lauderdale, Fl Miami Dade College, Miami, FL Arthur R. Williams, PhD, MA, MPA College of Public Health University of South Florida, Tampa, FL Michele Fix, RN, BSN Children s Mercy Hospitals and Clinics Kansas City, MO

2 Background The Joint Commission Safety Goal 9B Organizations must have a fall reduction program Interventions must be implemented to reduce patients fall risk factors Fall reduction strategies must be individualized Education & training regarding fall reduction programs must include Staff Patients/Families Program must be evaluated to determine effectiveness Evaluation of the patient population, settings & services Institute for Healthcare Improvement (IHI) 5 million Lives Campaign Validated the need to decrease the number of hospital medical injuries Falls Patient safety across the lifespan

3 Child Health Corporation of America (CHCA) Pediatric Falls Study Patricia Jamerson, PhD, RN (PI) 28 children s hospitals 782 pediatric patients fell during a 6 month period slipped, tripped and fell from their cribs, beds, chairs and examination tables 10 (1.3%) were dropped by staff or their parents

4 Falls Definitions Patient Falls-An unplanned descent to the floor, either with or without injury to the patient (ANCC Magnet Manual 2008). Adjusted Falls-An unintended event resulting in a person coming to rest on floor or other lower level (witnessed) or reported to have landed on the floor (un-witnessed) Falls Rate - total # of patient falls x 1,000 divided by total # of patient days (ANCC Magnet Manual 2008). Children At-High Risk For Falls Preschoolers Children > 10 are twice as likely to fall compared with the total population; age group is related to serious injury and death Children with disabilities and minimal mobility Children in wheelchairs, regardless of cognitive ability, due to tips and falls

5 King s Theory of Goal Attainment GROWTH AND DEVELOPMENT Pediatric falls differ according to stages of growth and development. PERCEPTION A process of organizing, interpreting and transforming information from sense data and memory; behavior flows from one s perceptions and influences one s behavior. COMMUNICATION Communication between nurse and pediatric patient and their family INTERACTION A process of perception and communication between person and environment, between person-to-person, represented by verbal and nonverbal behaviors that are goal directed TRANSACTION A process of interaction in which human beings communicate with environment to achieve Mutual Goal Setting (nurse with child and their parents).

6 This Study Purpose This study was part of a CHCA multi-site study in which 31 children who fell during a 6 month period in 2008 were submitted for data analysis Control subjects were not part of the CHCA study Reviewed cases for the year 2008 in this study 74 cases 242 inpatient randomly selected control cases 316 total cases of HDFS tm were retrieved.

7 Methods Used in This Study Research design: a retrospective observational case-control study Occurrence reports Electronic medical patient records used Pediatric fall cases differ from adult cases Developmental stages are germane in analyzing falls* infant (<1) toddler/pre-school (1-3) early childhood (3-6) middle childhood (6-12) teenagers (13-18). Hockenberry, M. & Wilson, D (eds) (2009) Wong s essentials of pediatric nursing 8 th ed; St Louis: Elsevier

8 Research Questions 1. Do children differ in their risk of a fall according to HDFS risk scores in a casecontrol study? 2. Do the cases/controls fall as predicted consistent with HDFS risk scores in a casecontrol study?

9 Developing a Pediatric Falls Scale Difficulties in developing a valid, easy to use and reliable risk assessment instrument Falls are relatively infrequent events in pediatrics Falls are associated with developmental stages, i.e. toddlers learning to walk and in a rush Falls may be related to psychosocial variables not just the medical condition of the child Failure to properly identify real risks on an risk scoring tool may arise due to effectiveness of existing Fall Prevention programs. Fall data is collected retrospectively and depends heavily upon accuracy of medical records

10 Falls Assessment Instruments Adult Tools Morse Fall Scale Hendrich Tinetti Pediatric Tools CHAMPS Graf-PIF Scale Hendrich II Humpty Dumpty Falls Scale (Miami Children s Hospital) I m Safe (The Children s Hospital of Denver) Phoenix Children s Falls Assessment Tool

11 Elaine Graf (Graf-PIF Scale) Children s Memorial Medical Center Length of Stay 1-4 days- score days- score days- score days- score 3 Children without an IV- score 1 PT/OT ordered- score 1 If prescribed anti-seizure Medication score 1 Acute or chronic Orthopedic diagnosis- score 1 Falls classification accidental falls unanticipated physiological falls (Morse, 1977) anticipated physiological falls Note: Reported sensitivity 75%; specificity 76% at a cut-point of 2, but not documented independent of tool developers and outside of original study setting.

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13 Humpty Dumpty English nursery rhyme Humpty Dumpty sat on a wall, Humpty Dumpty had a great fall All the king s horses and all the king s men Couldn't put Humpty Dumpty together again

14 Humpty Dumpty Falls Scale (HDFS) Age Gender Diagnosis Cognitive impairments Environmental Factors Response to Surgery / Sedation/Anesthesia Medication usage A patient is considered at risk for falls if score =>12 Range Maximum Score 23; Minimum Score 7 Components Criteria Score (circle) Age < >13 1 Gender Male 2 Female 1 Diagnosis Neurological Diagnosis 4 Alterations in Oxygenation 3 Psych / Behavioral 2 Other Diagnosis 1 Cognitive Not aware of limitations 3 Impairments Forgets limitations 2 Oriented to own ability 1 Environmental Factors History of Falls or Infant-Toddler placed in bed 4 Pt uses assistive devices or Infant-toddler in crib 3 Patient placed in bed 2 Outpatient area 1 Response to Within 24 hours 3 Surgery / Sedation Within 48 hours 2 Anesthesia More than 48 hours/none 1 Medication Usage Multiple usage of: Sedatives (excluding ICU patients sedated and paralyzed) Hypnotics Barbiturates Phenothizines Antidepressants Laxatives/Diuretics Narcotics One of the meds listed above 3 2 Other medications/none 1 Total Score

15 Results: Demographics Group Development al Level # Gender # Ethnicity Diagnosis Pediatric patients who fell (74) CASES >3 infants 3-7 toddlers /pre-school 7-13 school aged female Male Predominately Caucasians Less than 33% Blacks, Hispanic or Asian #1 Gastrointestinal #2 Neurologic/Development Delay #3 Oncology #4 Orthopedic 13+ teenagers 22 Pediatric patients who did not fall (242) CONTROLS >3 infants 3-7 toddlers /pre-school 7-13 school aged 13+ teenagers female Male Predominately Caucasians Less than 35% Blacks, Hispanic or Asian Other (middle eastern families do not mark themselves in these categories #1 Gastrointestinal #2 Oncology #3 Neurologic/Developmenta l Delay

16 Results All pediatric patients were assessed for falls Some actual falls not rated as High Risk using HDFS >=12 65% of control cases identified as at High Risk >=12. The OR was 1.15 with CI;.39, 3.15, p >.76. At a cut-off point of >=12 (High Risk per HDFS), sensitivity was 57%; specificity 39%. At a cut-off point of >=11 (not High Risk per present HDFS scoring), sensitivity was 75%; specificity 28%. Similar screening findings have been reported for Morse scale

17 A ROC CURVE ILLUSTRATING TRADE-OFFS BETWEEN SENSITIVITY AND SPECIFICTY Miami Children's data Legend; The top line is a hypothetical curve where sensitivity (vertical axis) =.80 and specificity (horizontal axis) =.80 at the inflection point. The solid line with the dots is the empirically determined HDFS ROC which equals only 54% of the area of the rectangle of this ROC curve.

18 HDFS Results The correlation between HDFS and age is r = (p<.001) An OLS regression indicated that age is highly associated with lower HDFS. Age alone in this regression accounts for 32% of the variance in HDFS These results suggest that age should be better accounted for than it is at present in the HDFS. However, the ages or age groups should be assigned empirically determined weights, e.g, a strictly linear relationship (straight line) may not identify risks of falling as well as a step function shown below. Rescoring of the HDFS with empirically determined weights of all risk items may improve results.

19 Ordinary Least Squares Regression: Age on HDFS scores age 95% CI Fitted values hdfs Legend: R 2 =0.346, F= , p<.001; coefficient for age in years = , t=-12.42, p<.001

20 Contributors to Pediatric Patient Falls and Fall-Related Injuries (Woods, et al., 2005) Fall Environment factors Biomechanical factors Parent s human factors Child s Human factors Preventable adverse events: falls, near falls, fall Injuries Risk of a Fall Nurse s human factors Latent system factors

21 Pediatric Fall Harm Index (PhFX) assesses the collective harm occurring from falls in an objective manner Weight Level of Harm Clinical Criteria 0 No Harm No Assessment, diagnostic testing or treatment required 1 Minimal Harm 2 Moderate Harm 3 Significant Harm Superficial assessment &/or treatment required for injury (i.e. cleaning of site, ice, bandage) Assessment, diagnostic testing, &/or treatment required for injury (i.e. laceration or fracture suspected or diagnosed) Assessment, diagnostic testing, &/or treatment required for permanent harm (i.e. brain/spinal injury, or death)

22 Fall Injury Rate Minor Harm, 19% Moderate Harm, 0% Major Harm, 0% No Harm, 81% Death, 0% Moderate Harm Major Harm Death Minor Harm No Harm Minor injuries (19%) in 74 falls

23 Discussion The HDFS captures some of the real risk of falling among hospitalized pediatric patients as shown by the odds ratio (OR) at a cut point of 12. However, further assessment of the instrument is needed for efficient screening. Far too many false positives are identified as indicated by the poor specificity of the instrument. The smaller number of false negatives also requires some fix to raise sensitivity about or above.80. Nurses must monitor pediatric patients frequently, record HDFS scores in EMR, for assessment, and implement preventive fall measures.

24 Discussion Nurses may not always observe their patients holistically but rely on the patient s present condition. Nurses may not take into account underlying factors that place patients at higher risk for falls such as behavior. The HDFS requires further testing and development to be a reliable screening tool, but this seems to be the case for other tools now available. At a minimum, weights of risk parameters should be empirically determined and more parameters may be needed on tools, particularly behavioral parameters. The HDFS is a promising tool for research and further development is needed in clinical settings, but the tool might be used in clinical settings if leaders and staff nurses are cognizant of its potential for over-identification of the risk status of patients.

25 Conclusion A nurse s best clinical judgment remains a valuable resource in decreasing the incidence of falls and related injury. Nurses and parents must increase their awareness of patient injuries from falls in order to provide safe, non-invasive care. Preventing falls is challenging due to pediatric patients cognition, growth, and development. Although pediatric fall rates are well below adult fall rates, falls may not be as carefully monitored in pediatric as compared to adult facilities. Fall rates are derived from voluntary reporting mechanisms and may vary due to reporting rather than the actual fall rate number per 1000 patient days. Preventing falls in pediatric populations is challenging due to the unpredictability of falls related to a child s age, cognition, growth, and development.

26 Lessons Learned Using the HDFS fall tool as part of pediatric assessment stresses the need for nurses best clinical judgment, a valuable resource in decreasing fall incidence/related injury. Identifying patients at-risk for falls ensures that all disciplines, parents and visitors increase their awareness of patient injuries in providing safe, noninvasive care. Interventions should be based on developmental levels. Enhance vigilance for all pediatric patients in room, hallway and playroom. Observations Parent-child interaction Parenting skills Behavior of child in presence of parent or nurse

27 Recommendations for Further Study Future studies with larger sample sizes across multiple institutions identifying children at risk on HDFS, focusing on preventing serious injury Attention needs to be given to the weights assigned to parameters on the HDFS and the possible incorporation of parental presence or even parental risks behavioral characteristics of the patient. Improve the scoring algorithm Use a greater number of potentially predictive variables Use the HDFS prospectively with patients to determine clinically relevant screening properties. Why would we (pediatric nurses) expect parents/guardians to heed our words on children s safety while in their care, if we can not keep them safe while in our care? Decreasing number of pediatric falls and implementing falls prevention programs is every pediatric nurse s responsibility

28 Review of Literature Child Health Corporation of America (2009). Pediatric falls: State of the science. Pediatric Nursing 35(4), Frankenburg, W.K., Camp, B.W. (1975). Pediatric screening tests. Springfield, IL: Thomas. Graf, E. (2011). Magnet Children s Hospitals: leading knowledge development and quality standards for inpatient pediatric fall prevention programs. Journal of Pediatric Nursing 26(2), 4-5. Haynes, R.B., Sackett, D.L., Guyatt, G.H., Tugwell, P. (2006). Clinical epidemiology: how to do clinical practice research. 3rd ed; Boston, MA: Lippincott, Williams & Wilkins. Hendrich, A, Bender, P, Nyhuis, A. (2003). Validation of Hendrich II Fall Risk Model: A large concurrent case/control study of hospitalized patients. Applied Nursing Research, 16(1), Hockenberry, M. & Wilson, D. (eds) (2009) Wong s essentials of pediatric nursing 8th ed. St Louis. Elsevier. Hill-Rodriguez, D., Messmer, P.R., Williams, P.D., Zeller, R.A., Williams, A.R., Wood, M. & Henry, M/ (2009). Humpty Dumpty Falls Scale: A case control study. JSPN 14(1), Kingston, F., Bryant, T., & Speer, K. (2010). Pediatric falls benchmarking collaborative JONA 40(6),

29 Review of Literature Meyers, H.& Nikoletti, S. (2003). Fall risk assessment: A prospective investigation of nurses clinical judgment and risk assessment tools in predicting patient falls. IJNP, 9, Monson S. et al (2008). In-hospital falls of newborn infants: data from multihospital healthcare system Pediatrics 122(2) e277-e280. Morse, J.M. (2006). The safety of safety research: the case of patient fall research. Can J Nurs Res 38(2), Neiman, J. & Rannie, M. I m safe: development of a fall prevention program to enhance quality and patient safety. 8th NICHQ conference, Grapevine, TX, March 11, O Connell, B., Myers, H. (2002). Research in brief. The sensitivity and specificity of Morse Fall Scale in an acute care setting. Journal of Clinical Nursing, 11(1), Pillai, S.B.(2000). Fall injuries in pediatric population: Safer and most cost effective management. Journal Trauma, 48(6), Raszmus, I. (2006). Falls in hospitalized Children. Pediatric Nursing 32(6), Robles, J. (2009). The effect of the electronic medical record. Creative nursing. 15(1), Woods et al. (2005). Anatomy of a patient safety event: a pediatric safety taxonomy Qual Saf Health Care 15,

30 Thank You!!! Patricia R. Messmer, PhD, RN-BC, FAAN Arthur R. Williams, PhD, MA, MPA Michele Fix, RN, BSN