Nursing Diagnoses & Outcome Calculation:

Transcription

1 Nursing Diagnoses & Outcome Calculation: a new perspective for nursing process documentation and evaluation Nursing Documentation and Length of Stay in Orthopedic Surgery Dr. Wolter Paans Dr. Maria Muller-Staub Dr. Wim Krijnen Hanze University of Applied Sciences, Groningen, The Netherlands 1

2 Research Question 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, Titel presentatie aanpassen 4

3 Method Review of 300 records in hip prostheses patients. Review of 604 records in knee prostheses patients. Two orthopedic units, one Dutch hospital Review: September February 2016 Reference: NANDA-I nursing diagnoses Titel presentatie aanpassen 5 3

4 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.

5 Data collection & instrument 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 1 Paans, 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), Paans, 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), , published on line: doi: /j x Titel presentatie aanpassen 6 5

6 Nursing Diagnoses & L.O.S. In Hip Patients Nursingdiagnosis (n=300records) %(n) Diagnosed Mean(SD)L.O.S. Notdiagnosed P;value* 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 Pressureulcer 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 ImbalancedNutrition/less thanbodyrequirements Imbalancedfluidvolume/ deficientfluidvolume 14(42) 14,23(9,615) 9,46(5,074) 0,011 12(37) 15,57(10,265) 9,32(4,779) 0,004 Impairedtissueperfusion TotalNDxN=613/300rec. Mediandischargeon9 th day includingdayof admission/discharge A IndependentSamplesT;test B L.O.S *P=<0,05 13(38) 15,34(9,382) 8,99(4,451) <0,000

7 Medical treatment: Knee Prostheses Protheses(( (n(=(606)( %((n)( (( Mean((SD)(L.O.S.((( P7value( ( 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)( ( ( ( A (Kruskal7Wallis(H(test( B (L.O.S( *(P(=(<(0,05( (

8 Medical treatment & L.O.S. Hip Patients Medicaltreatment (n=300) %(n) Mean(SD)L.O.S P9value* 0,051 Dynamichipscrew(DHS) 18(47) 11,11(7,403) Cannulahipscrews 11(29) 8,59(3,647) Gammanail 49(128) 10,39(6,786) Hemiarthroplasty(hip prosthesis) Othertreatments Missingvaluesn=38 A Kruskal9WallisHtest B L.O.S *P=<0,05 12(32) 9,22(4,680) 10(26) 9 9 9

9 Readmissions and L.O.S. In Knee patients Readmissions* (n*=*604)* %**(n)* Mean*(SD)*LOS** P8value* Readmitted* 5,12****(31)* 6,23*(4,57)* 0,001** Not*Readmitted* 94,88*(573)* 4,87*(3,04)* * * A *Independent*Samples*T8test* B *L.O.S.* **P*=*<*0,05*

10 Difference in Length of Stay (LOS) hip patients and medical diagnoses ᴬ Independent samples T-test ᴮ Dependent variable: LOS, p<.05. Medicaldiagnoses (Cases:n=300) %(n) Mean(SD)L.O.S. P;value Diagnosed Notdiagnosed Lungdisease 18(55) 11,31(7,643) 9,90(5,778) 0,0457* Cadiacdisease 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 16(47) 12,03(8,241) 9,80(5,635) 0,0430* ComorbidityMedicalDx N=275/300 Titel presentatie aanpassen

11 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 both groups 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 to medical 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, and strong predictive on L.O.S).

12 Conclusions 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) Titel presentatie aanpassen 14

13 Conclusions Diagnostic information: T1 (diagnostic assessment information): poor, T2 (diagnostic post surgery information): moderate, T3 (diagnostic discharge / hand over information) poor Titel presentatie aanpassen 14 13

14 To make it easier: Tools for Big Data Computing Data Warehouse The Data Warehouse is a stable, read-only database that combines information from separate systems into one, easy-to-access location. It is a place where data is stored for archival, analysis, and security purposes. Cube - an instrument for researchers, manager and professionals - software that analyzes data to identify relationships and to gain insights 14

15 Needs for Big Data Computing 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) Titel presentatie aanpassen 14 15

16 Recommendations for clinical practice 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) Titel presentatie aanpassen 16 16

17 Measurement links 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) Titel presentatie aanpassen 14 17

18 "The cube" Dimension area the results selection area Dr. W. Müller-Staub, Paans Hanze Odenbreit University & Paans Groningen 18

19 Results Area Domain: comfort nursing diagnoses Wards 19

20 Combinations You can combine all data from your patients Age, gender, medical diagnoses, nursing diagnoses, treatment / interventions, outcomes, LOS, costs, safety indicators. To get the right answers, you must combine the correct data Which treatment has the best effect on (-)? What is correlated to nursing? 20

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