RELATION BASED CARE - Eurac Research24.11.16 Dr. W. PaansHanze University Groningen RELATION BASED...

Dr. W. Paans Hanze University Groningen24.11.16

RELATION BASED CARE

18th November 2016 at the EURAC in Bolzano

Nursing Diagnoses and Length of Stay in Orthopedic Surgery

Dr. Wolter PaansDr. Maria Muller-Staub

Dr. Wim KrijnenHanze University of Applied Sciences, Groningen,

The Netherlands

1


Research Group in Nursing Diagnostics Intro.

Research topics:

• Communication, Critical Reasoning, PatientInvolvement, Documentation and Handover

• Digital Health

• Family Care / Relation Based Care

Research seen from a holistic, systemic point of view

Dr. W. Paans Hanze University Groningen24.11.16Titel presentatie aanpassen 4

Research Question

17.01.16

What is the predictive power of nursing diagnosis documentation in the patient record on Length Of Hospital Stay (LOS)?

Hip prostheses patients; age of > 65, admitted in hospitals for surgery.Knee prostheses patients; age of > 65, admitted in hospitals for surgery,

. 3


Method

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Review of 300 records in hip prostheses patients.Review of 604 records in knee prostheses patients.

Two orthopedic units, one Dutch hospital

Review: September 2014 - February 2016

Reference: NANDA-I nursing diagnoses

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Sample

Review of 300 patient records in hip prostheses patients: mean (SD) age: 76 (11) 220 female, 80 male.

Review of 604 patient records in knee prostheses patients: mean (SD) age: 69 (8) 413 female, 191 male.


Data collection & instrument

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• Measurement: first day Post-Surgery (PS)

• Instrument: D-Catch instrument developed for the analysis of the accuracy in nursing documentation.

Reference: NANDA-I, NIC, NOC

1Paans, W., Sermeus, W., Nieweg, M.B., Schans, van der, C.P. (2010). Psychometric properties of the D-Catch instrument, an instrument for evaluation of the nursing documentation in the patient record, Journal of Advanced Nursing, Nursing, 66 (6), 1388-1400.

1Paans, W., Sermeus, W., Nieweg, M.B., Schans, van der, C.P. (2010). Prevalence and accuracy of nursing documentation in the patient record. Journal of Advanced Nursing, 66 (11), 2481-2490, published on line: doi:10.1111/j.1365-2648.2010.05433.x

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Nursing Diagnoses & L.O.S. In Hip Patients

Nursing diagnosis (n= 300 records)

% (n) Mean (SD) L.O.S.

P‐value*

Diagnosed Not diagnosed

Pain 70 (210) 10,92 (6.589) 7,35 (3,207) <0,000

Disordered / Distressed 42 (126) 11,42 (7,564) 9,07 (4,382) 0,008

Pressure ulcer 18 (55) 14,72 (8,833) 8,96 (4,594) <0,000

Obstipation 20 (60) 12,73 (7,958) 9,46 (5,428) 0,011

Anxiety 15 (45) 12,23 (7,585) 9,77 (5,828) 0,018

Imbalanced Nutrition /less than body requirements

14 (42) 14,23 (9,615) 9,46 (5,074) 0,011

Imbalanced fluid volume / deficient fluid volume

12 (37) 15,57 (10,265) 9,32 (4,779) 0,004

Impaired tissue perfusion

Total NDx N= 613 /300 rec.

Median discharge on 9th day including day of admission/discharge

A Independent Samples T‐test B L.O.S * P = < 0,05

13 (38)

15,34 (9,382) 8,99 (4,451) <0,000

Dr. W. Paans Hanze University Groningen24.11.16Titel presentatie aanpassen 917.01.16

Nursing Diagnoses & L.O.S. In Knee Patients

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Nursing Diagnoses (n = 604 records)

% (n)

Mean (SD) L.O.S Diagnosed Not Diagnosed

P‐value*

Impaired tissue perfusion 16,0 (97) 5.44 (3.31) 4.85 (3.12) <0,000

Disoriented / Distressed 3,80 (23) 6,48 (2,57) 4,88 (3,16) <0,000

Nausia 18,15 (110) 6,75 (4,93) 4,54 (2,42) <0,000

Anxiaty 3,30 (20) 5,35 (2,71) 4,93 (3,16) 0,001

Pain in rest situation Delirium Pressure Ulcer Obstipation Diarrhea Total NDx: N= 551/604 rec. Median discharge on 4th day (Including day of admission & discharge)

31,35 (190) 1,16 (7) 2,31 (14) 9,74 (59) 5,12 (31)

6,54 (3,01) 9,71 (2,81) 10,07 (5,03) 7,59 (2,78) 9,61 (4,45)

4,67 (3,18) 4,89 (3,11) 4,82 (3,00) 4,65 (3,05) 5,73 (3,30)

0,011 0,029 <0,000 <0,000 <0,000

A Independent Samples T‐test B L.O.S * P = < 0,05


Pain measurements and L.O.S. In Knee patients

Pain scores VNRS / VAS (n = 604 records)

% (n)

Mean (SD)

P‐value

0‐3 60,89 (369) 4,76 (3,38) 0,030* 4‐7 21,12 (128) 5,27 (3,09) 8‐10 2,31 (14) 5,71 (2,55) Missing Values

15,68 (93)

A Kruskal‐Wallis H test B L.O.S. * P = < 0,05


Medical treatment & L.O.S. Knee Patients

Treatment (n = 604)

% (n)

Mean (SD) L.O.S.

P‐value

Rapid Recovery 45,00 (270) 4,80 (1,93)

0,000*

Joint Care 24,00 (116) 6,33 (2,41)

Regular treatment Missing values n=38

31,00 (180) 7,90 (5,20)

A Kruskal‐Wallis H test B L.O.S * P = < 0,05


Medical treatment: Knee Prostheses

Protheses (n = 606)

% (n)

Mean (SD) L.O.S. P‐value

Total Knee prostheses 68,32 (414) 4,62 (2,44) 0,000* Total Knee prostheses with

Patella prostheses 12,87 (78)

5,90 (2,74)

Demi Knee prostheses 2,15 (13) 2,54 (0,97) Patella prostheses 8,75 (53) 4,51 (1,97) Revision Total Knee prostheses 5,45 (33) 8,36 (7,69) Revision Totale Knee with Patella Prostheses

0,66 (4)

8,75 (6,95)

Revision Patella prostheses 1,49 (9) 3,78 (1,41) Missing Values 0,33 (2)



Medical treatment & L.O.S. Hip Patients

Medical treatment (n=300)

% (n) Mean (SD) L.O.S

P‐value*

0,051

Dynamic hip screw (DHS) 18 (47) 11,11 (7,403)

Cannula hip screws 11 (29) 8,59 (3,647)

Gamma nail 49 (128) 10,39 (6,786)

Hemi arthroplasty (hip prosthesis)

12 (32) 9,22 (4,680)

Other treatments

Missing values n= 38


10 (26) ‐ ‐ ‐


Readmissions and L.O.S. In Knee patients

Readmissions (n = 604)

% (n) Mean (SD) LOS P‐value

Readmitted 5,12 (31) 6,23 (4,57) 0,001*

Not Readmitted 94,88 (573) 4,87 (3,04)

A Independent Samples T‐test B L.O.S. * P = < 0,05


Titel presentatie aanpassen 11

Difference in Length of Stay (LOS) hip patients and medical diagnoses

ᴬ Independent samples T-testᴮ Dependent variable: LOS, p<.05.

19-01-1614

Medical diagnoses (Cases: n= 300)

% (n)

Mean (SD) L.O.S.

P‐value

Diagnosed Not diagnosed

Lung disease 18 (55) 11,31 (7,643) 9,90 (5,778) 0,0457*

Cadiac disease 41 (123) 11,00 (7,152) 9,11 (4,475) 0,0027*

CVA (Stroke) 7 (21) 14,20 (8,170) 9,88 (5,948) 0,0405*

Diabetes

Co morbidity Medical Dx N= 275 / 300

16 (47) 12,03(8,241) 9,80 (5,635) 0,0430*


Titel presentatie aanpassen 11

Difference in Length of Stay (LOS) knee replacement and medical diagnoses

ᴬ Independent samples T-testᴮ Dependent variable: LOS, p<.05.

19-01-1615

A Independent Samples T‐test

Comorbidity / medical Dx. N= 433 /604

B L.O.S. * P = < 0,05 T‐test

Comorbidity (n = 604) % (n)

Mean (SD) LOS Diagnosed Not Diagnosed

P‐value

Diabetes 16 (99) 5,54 (5,03) 4,83 (2,62) 0,011* Hart failure 26 (155) 5,90 (4,65) 4,61 (2,35) 0,000* Lung disease 15 (95) 4,73 (5,03) 4,80 (2,64) 0,003* Tractus Digestivus disease 8 (46) 6,30 (3,79) 4,83 (3, 07) 0,016* Thrombosis 6 (38) 6,82 (6,88) 4,82 (2,69) 0,001*

Dr. W. Paans Hanze University Groningen24.11.16Titel presentatie aanpassen 1419-01-16

Results from modeling days hospitalized by Poisson regression in terms of the estimated parameters, their standard errors (SE), t-value, significance measured by

p-value, the rate ratio and their 95% Confidence interval.

Hip sample

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Estimate SE t.value P‐value

Exp

Estimate CLL CLR

(Intercept) 1,2688 0,3657 3,47 0.0000 3,5567 1,7312 7,2595

Age 0,0103 0,0043 2,3788 0,0181 1,0104 1,0018 1,019

Impaired tissue perfusion

(surgical wound area) 0,3423 0,0768 4,46 0,0000 1,4082 1,2091 1,6338

Pressure ulcer 0,2607 0,0808 3,2261 0,0014 1,2979 1,1059 1,5183

Deficient fluid volume 0,3464 0,0899 3,8546 0,0000 1,414 1,1828 1,6826

Diabetes 0,214 0,0672 3,1848 0,0016 1,2386 1,0843 1,4111

Dr. W. Paans Hanze University Groningen24.11.16Titel presentatie aanpassen 1419-01-16

Results from modeling days hospitalized by Poisson regression in terms of the estimated parameters, their standard errors (SE), t-value, significance measured by

p-value, the rate ratio and their 95% Confidence interval.

Knee sample

17

Estimate SE T‐Value P‐Value Exp Estimate

CLL CLR

(Intercept) 1,2892 0,0268 45,1260 < 0.0000 3,6299 3,4307 3,8373 Medical Treatment

0,3755

0,0625

6,0056

< 0,0000

1,4557

1,2867

1,6440

Pressure Ulcer

0,4024 0,1025 3,9261 0,0001 1,4954 1,2172 1,8196

Thrombosis

0,2783 0,0693 4,0140 0,0001 1,3209 1,1503 1,5098

Inpaired Tissue Perf.

0,1502 0,0510 2,9475 0,0032 1,1621 1,0505 1,2828

Nausea

0,2393 0,0460 5,1986 < 0,0000 1,2704 1,1601 1,3896

Delirium

0,6232 0,1389 4,4860 < 0,0000 1,8649 1,4055 2,4251

Obstipation 0,2704 0,0559 4,8389 < 0,0000 1,3105 1,732 1,4606


Comparing diagnostics in the hip & knee sample

• Nursing diagnoses and comorbidity are more prevalent in hip patients compared to knee patients

• Pain is the most prevalent nursing diagnosis in both groups

• Nursing Diagnoses Impaired Tissue Perfusion and Pressure Ulcer are strong predictors of L.O.S. in bothgroups

• Medical treatment in knee patients is a strong predictor of L.O.S. (< L.O.S = Rapid Recovery)

• Medical treatment in hip patients is not a significant predictor of L.O.S.

• Nursing Diagnoses: Pain, Impaired Tissue Perfusion, Disordered / Distressed, Pressure Ulcer, Obstipation and Anxiety are significantly related to increased L.O.S. in both groups

• Nursing Diagnosis Nausia is a strong predictor of L.O.S. in the knee group, and significantly related tomedical treatment (rapid recovery v.s. other treatments: P value <0,000)

• The nursing diagnosis Deficient Fluid Volume is a strong predictor of L.O.S. in the group of hip patients

• Thrombosis (med. diagnosis) can be seen as a risk factor in the knee patient group (prev. 38 / 604, andstrong predictive on L.O.S).


Conclusions

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Nursing interventions are documented with low accuracy; the effect on outcomes is (still) not to measurable.

Relationship between nursing diagnoses and nursing actions / interventions, as well as the effect of nursing interaction is

hard to measure as the nature of the documentation is descriptive and not systematically (sometimes diffuse /

cryptic, unclear and redundant in nature)

19.12.15 19


Conclusions

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Diagnostic information:

T1 (diagnostic assessment information): poor,

T2 (diagnostic post surgery information): moderate,

T3 (diagnostic discharge / hand over information) poor.

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Needs for Big Data Computing

17.01.16

Technical improvements in the EHR are needed, i.e. output calculation possibilities:

- Nursing Process Decision Support Systems (NPDSS)Implementation of the use of definitions and classificatons

- Nanda-I, NIC, NOC for accuracy and efficiency in documentation

- Trans-sectorial care cooperation developments

- E-Health / interoperability to foster data exchange

- The use of new technologies (QS).

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Recommendations for clinical practice

17.01.16

Nursing Process - Clinical Decision Support Systems (NP-CDSS) are needed.

Nursing Process-Decision Support allows to retrieve Standardized Nursing Data from Electronic Health Records such as nursing diagnoses and hospital duration (LOS).

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Measurement links

17.01.1623

Linkages of sensor techniques and nursing diagnoses in the PES structure (SSEP‐I‐O): signs detections by sensor

second skin applications as a validation of nurses’ observation)


RELATION BASED CARE

Essentials:

• Involvement of the patient and relatives is essential (info. accuracy)

• Critical reasoning skills are essential

• Communication skills are essential

• Documentation skills are essential

• Classification is essential

• DDSS’ are essential

An holistic approach in relation based nursing is essential


Related publication (free tekst)

An Internationally Consented Standard for Nursing Process-Clinical Decision Support Systems in Electronic Health Records.

Müller-Staub M, de Graaf-Waar H, Paans W.

Comput Inform Nurs. 2016 Nov;34(11):493-502.

PMID: 27414705

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17.01.16

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