multidisciplinarytreatments,patientcharacteristics,context...

Hindawi Publishing CorporationNursing Research and PracticeVolume 2012, Article ID 350830, 14 pagesdoi:10.1155/2012/350830

Research Article

Multidisciplinary Treatments, Patient Characteristics, Contextof Care, and Adverse Incidents in Older, Hospitalized Adults

Leah L. Shever1 and Marita G. Titler2

1 Nursing Research, Quality, and Innovation, University of Michigan Health System,300 North Ingalls, Room NI 5A07, Ann Arbor, MI 48109-5446, USA

2 University of Michigan School of Nursing and University of Michigan Health System,400 North Ingalls, Suite 4170, Ann Arbor, MI 48109-5482, USA

Correspondence should be addressed to Leah L. Shever, [email protected]

Received 29 September 2011; Revised 3 December 2011; Accepted 3 December 2011

Academic Editor: John Daly

Copyright © 2012 L. L. Shever and M. G. Titler. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

The purpose of this study was to examine factors that contribute to adverse incidents by creating a model that included patientcharacteristics, clinical conditions, nursing unit context of care variables, medical treatments, pharmaceutical treatments, andnursing treatments. Data were abstracted from electronic, administrative, and clinical data repositories. The sample included olderadults hospitalized during a four-year period at one, academic medical facility in the Midwestern United States who were at risk forfalling. Relational databases were built and a multistep, statistical model building analytic process was used. Total registered nurse(RN) hours per patient day (HPPD) and HPPDs dropping below the nursing unit average were significant explanatory variablesfor experiencing an adverse incident. The number of medical and pharmaceutical treatments that a patient received duringhospitalization as well as many specific nursing treatments (e.g., restraint use, neurological monitoring) were also contributorsto experiencing an adverse incident.

1. Background

The Institute of Medicine (IOM) report To Err is Human[1] revealed the number and significance of adverse eventsand errors that occur during hospitalization. The report wasa call to action to transform healthcare systems to ensurepatient safety and higher quality care. In one step towardhealthcare transformation, the Centers for Medicare andMedicaid (CMS) no longer reimburses institutions for thecare, or treatment, associated with certain hospital-acquiredconditions [2].

Understanding what factors contribute to adverse inci-dents during hospitalization is essential to developing effec-tive counter measures. In order to improve factors that aremodifiable within a hospital structure or with healthcaredelivery, it is important to first have an understanding ofwhat is broken. There are a number of potential contributingfactors that need to be considered such as the patient’scondition, the care the patient receives, and the environmentin which they receive care [3, 4].

Battles and Lilford [3] provide a conceptual model forpatient safety that includes antecedent conditions, whichwould include the patient’s comorbid conditions, the pri-mary reason the patient was admitted to the hospital,and characteristics the patient possessed before enteringthe hospital. Their model also includes the structure, orenvironment, in which the patient receives care such as thehospital, or nursing unit. Also acting within the structureare the processes of care (the interventions or treatments)delivered by the multidisciplinary team caring for the patientin the hospital. None of these components exist in isolation,which is why it is important to examine all of these factorsand how they interact [3].

2. Purpose

The purpose of this study was to examine factors that con-tribute to adverse incidents that occur during hospitalizationby creating a model that included patient characteristics,clinical conditions, nursing unit context of care variables,

2 Nursing Research and Practice

Block 3: context of careNumber of units where patient resided RN nursing skill mixAverage hours per patient dayRN/patient dip proportion

Block 4: medical, number, and type Block 5: pharmacy, number, and type

Block 6: nursing, number, and type

Block 1: patient characteristicsAgeGenderEthnicityOccupationSite admitted from

Block 2: clinical conditionMedical diagnosisComorbiditySeverity of illnessPast hospitalizations

OutcomeAdverse incidents

Interventions or processes of care

Figure 1: Model for predicting adverse incidents in the hospital.

medical treatments, pharmaceutical treatments, and nursingtreatments. The research question addressed in this studyis: what patient characteristics, clinical conditions, contextof nursing care variables (e.g., nursing hours per patientday, RN skill mix, number of units resided on duringhospitalization), and treatments (medical, pharmaceutical,and nursing treatments) explain the occurrence of adverseincidents for hospitalized, older adults at risk for falling? Amodel that has been used successfully to guide multidisci-plinary effectiveness research in the hospital setting can beseen in Figure 1 [5, 6].

3. Methods

Data for this exploratory study came from a large, healthservice effectiveness grant [7] and was approved by theinstitution’s Human Subjects review board. Data from a four-year period (July 1, 1998 to June 31, 2002) were extractedfor the primary study from one large Midwestern academicmedical center. Data sources came from nine electronic datarepositories, including the nursing information system thatused the Nursing Interventions Classification (NIC) [8] toelectronically document nursing care delivered. Detail of thenine electronic repositories and methods to assure validityand reliability are discussed elsewhere [5]. Extracted datawere stored in a structured query language (SQL) serverand relational databases were built using a unique subjectnumber.

3.1. Sample. The inclusion criteria were hospitalizationsto one Midwestern tertiary care hospital over a four-yearperiod, patients 60 years of age or older upon admission,and at risk of falling. Patients were determined to be atrisk of falling based on a fall risk assessment [6] that wascompleted upon admission or when the patient receivedthe nursing intervention of Fall Prevention as recorded inthe electronic documentation system. Patients at risk forfalling were selected with the rationale that they would beat risk for experiencing one adverse incident (i.e., falling),and therefore interventions would be initiated to preventthe adverse incident. In addition, the hospitalizations wereselected as the unit of analysis rather than individual patientsand a variable was included to control for patients who hadmore than one hospitalization.

3.2. Study Variables. Conceptual and operational definitionsfor the independent variables included in the explanatorymodel are displayed in Table 1 and organized by the concep-tual model seen in Figure 1 (patient characteristics, clinicalconditions, context of care, and treatments). When appropri-ate, the source used to guide coding of variables is provided;for example, pharmaceutical treatments, or medications,were coded using the American Hospital Formulary Service(AHFS) codes [9].

The dependent variable for this analysis was the firstoccurrence of an adverse incident during an episode of hos-pitalization. Adverse incidents were defined as any undesired

Nursing Research and Practice 3

Ta

ble

1:In

depe

nde

nt

vari

able

defi

nit

ion

s.

Var

iabl

en

ame

Var

iabl

ede

fin

itio

nan

dco

din

gso

urc

eV

aria

ble

type

and

oper

atio

nal

defi

nit

ion

Pati

ent

char

acte

rist

ics

Gen

der

Th

ebe

hav

iora

l,cu

ltu

ral,

and

psyc

hol

ogic

altr

aits

typi

cally

asso

ciat

edw

ith

one’

sse

x.C

ateg

oric

al:M

=m

ale,

F=

fem

ale,

D=

defe

rred

(not

dete

rmin

edye

t).

Age

Age

wh

enpa

tien

tw

asad

mit

ted

toh

ospi

tal.

Con

tin

uou

s;m

easu

red

inye

ars.

Occ

upa

tion

Act

ivit

ypu

rsu

edas

aliv

elih

ood.

Cat

egor

ical

:1=

reti

red,

2=

wor

kin

g/em

ploy

ed,3

=h

omem

aker

,4=

not

reti

red/

not

empl

oyed

.

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nic

ity

Rac

e:a

grou

pof

peop

leu

nit

edby

cert

ain

char

acte

rist

ics.

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egor

ical

:1=

Cau

casi

an,2

=al

loth

ers

(in

clu

des

the

cate

gori

esof

Afr

ican

Am

eric

an,H

ispa

nic

,Nat

ive

Am

eric

an/A

lask

anN

ativ

e;A

sian

/Pac

ific

Isla

nde

r,an

dot

her

).

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adm

itte

dfr

omT

he

site

from

wh

ich

the

pati

ent

was

adm

itte

dto

the

hos

pita

l.C

ateg

oric

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=h

ospi

tal,

2=

care

faci

lity,

3=

hom

e/ot

her

rou

tin

ead

mis

sion

.C

linic

alco

ndi

tion

s

Pri

mar

ym

edic

aldi

agn

osis

Th

epr

imar

ym

edic

aldi

agn

oses

cam

efr

omth

eIn

tern

atio

nal

Cla

ssifi

cati

onof

Dis

ease

s,9t

hR

evis

ion

(Clin

ical

Mod

ifica

tion

)(I

CD

-9-C

M)

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0]fo

un

din

MR

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agn

osti

cco

des

and

hav

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encl

assi

fied

into

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ical

Cla

ssifi

cati

onSo

ftw

are

(CC

S)ca

tego

ries

[11]

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hot

omou

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no,

the

diag

nos

is(i

.e.,

asre

pres

ente

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CS

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prim

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(i.e

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repr

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lar

CC

Sca

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rity

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nes

s

Ara

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gas

sign

edto

each

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pect

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sure

orga

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stem

loss

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nct

ion

orph

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easo

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rh

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ifica

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and

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urc

eu

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3].

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hof

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mor

bid

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ical

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diti

ons

istr

eate

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chot

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le:

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no,

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diti

onw

asn

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esen

tat

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issi

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1=

yes,

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onw

aspr

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tat

the

tim

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adm

issi

on.

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hos

pita

lizat

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sdu

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gth

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udy

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odT

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iou

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taliz

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pati

ent

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rien

ced

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ng

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dype

riod

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gral

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prev

iou

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atio

ns,

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evio

us

hos

pita

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ion

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evio

us

hos

pita

lizat

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3pr

evio

us

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ion

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4or

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ter

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taliz

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text

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riab

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rage

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en

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es[1

4]fo

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sit.

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eh

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RR

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lues

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talR

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ours

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per

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me

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tim

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ofth

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ours

for

that

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and

then

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ula

te:

sum

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lues

for

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enti

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ours

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n.

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gsk

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sto

alln

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for

asp

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me

per

iod

.

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eav

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the

hou

rly

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valu

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asob

tain

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din

gth

eto

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um

ber

ofR

Ns

for

allh

ours

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eto

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ours

for

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hos

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he

aver

age

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iver

hou

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asob

tain

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din

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eto

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um

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vers

for

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ours

byth

eto

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um

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ofh

ours

for

the

hos

pita

lvis

it.


Ta

ble

1:C

onti

nu

ed.

Var

iabl

en

ame

Var

iabl

ede

fin

itio

nan

dco

din

gSo

urc

eV

aria

ble

type

and

oper

atio

nal

defi

nit

ion

CG

PR

RN

dip

vari

able

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eex

ten

tto

wh

ich

the

min

imu

mam

oun

tof

RN

care

falls

belo

wth

eav

erag

eof

allt

he

hou

rly

CG

PR

valu

esfo

rth

een

tire

visi

t.T

his

repr

esen

tsth

eva

riab

ility

inth

eam

oun

tof

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care

that

isav

aila

ble,

spec

ifica

llyth

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ten

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wh

ich

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amou

nt

ofR

Nca

reav

aila

ble

drop

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low

the

aver

age

amou

nt

ofR

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reav

aila

ble

for

the

hos

pita

lvis

it.

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rage

CG

PR

-RN

min

us

the

aver

age

ofth

eth

ree

low

est

hou

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CG

PR

RN

valu

esfo

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evi

sit.

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ela

rger

this

valu

eis

,th

em

ore

the

min

imu

mC

GP

RR

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llbe

low

the

aver

age

for

the

visi

t.

Nu

mbe

rof

un

its

resi

ded

onT

he

sum

ofth

en

um

ber

ofu

nit

son

wh

ich

trea

tmen

tw

aspr

ovid

edto

anin

divi

dual

pati

ent

duri

ng

the

cou

rse

ofth

eh

ospi

talv

isit

.In

tegr

al:

1=

1u

nit

,2=

2u

nit

s,3=

3u

nit

s,4=

4u

nit

s,5=≥

5u

nit

sTr

eatm

ents

Nu

mbe

rof

med

ical

trea

tmen

ts

Med

ical

proc

edu

res

perf

orm

eddu

rin

ga

hos

pita

lvis

itto

diag

nos

ean

dtr

eat

agi

ven

pati

ent

base

du

pon

aph

ysic

ian’

sju

dgm

ent

and

know

ledg

eto

prom

ote

orm

ain

tain

hea

lth

,cu

redi

seas

es,o

rpa

lliat

ein

cura

ble

dise

ases

.Cod

edu

sin

gIC

D-9

-CM

code

s[1

0]fr

omth

em

edic

alre

cord

abst

ract

ion

(MR

A)

and

regr

oupe

din

tom

ult

ileve

lC

CS

cate

gori

es[1

1].

Con

tin

uou

s:a

cou

nt

onth

en

um

ber

ofm

edic

altr

eatm

ents

that

wer

epe

rfor

med

duri

ng

the

cou

rse

ofa

hos

pita

lvis

it,t

his

isn

otth

en

um

ber

ofu

niq

ue

med

ical

trea

tmen

ts.

Typ

esof

med

ical

trea

tmen

ts

Any

proc

edu

reth

at,b

ased

upo

na

phys

icia

n’s

judg

men

tan

dkn

owle

dge,

isn

eces

sary

topr

omot

eor

mai

nta

inh

ealt

h,c

ure

dise

ases

,or

palli

ate

dise

ase

proc

esse

sth

atar

ein

cura

ble.

Cod

edu

sin

gIC

D-9

-CM

code

s[1

0]fr

omth

eM

RA

and

regr

oupe

din

tom

ult

ileve

lC

CS

cate

gori

es[1

1].

Dic

hot

omou

s:0=

no

the

trea

tmen

t(i

.e.,

asre

pres

ente

dby

apa

rtic

ula

rC

CS

cate

gory

)w

asn

otre

ceiv

eddu

rin

gh

ospi

taliz

atio

n,1

=ye

s,th

etr

eatm

ent

(i.e

.,as

repr

esen

ted

bya

part

icu

lar

CC

Sca

tego

ry)

was

rece

ived

atle

ast

once

duri

ng

hos

pita

lizat

ion

.

Nu

mbe

rof

un

iqu

em

edic

atio

ns

Th

eco

un

tpe

rvi

sit

ofu

niq

ue

gen

eric

dru

gn

ames

for

dru

gsad

min

iste

red

atle

ast

once

duri

ng

avi

sit.

Med

icat

ion

typ

esw

ere

code

du

sin

gth

eA

mer

ican

Hos

pita

lFor

mu

lary

Serv

ice’

s(A

HFS

)th

ree-

leve

lsys

tem

[9].

Con

tin

uou

s:a

cou

nt

ofth

en

um

ber

ofu

niq

ue

med

icat

ion

sde

liver

eddu

rin

ga

hos

pita

lvis

it.

Ph

arm

acy

trea

tmen

tsM

edic

atio

ns

use

din

the

care

ofpa

tien

tsdu

rin

ga

hos

pita

lvis

it.

Med

icat

ion

typ

esw

ere

code

du

sin

gth

eA

HFS

thre

e-le

vels

yste

m[9

].

Dic

hot

omou

s:0=

no

med

icat

ion

from

the

AH

FScl

ass

was

adm

inis

tere

ddu

rin

gth

eh

ospi

talv

isit

,1=

yes,

atle

ast

one

med

icat

ion

from

the

AH

FScl

ass

was

adm

inis

tere

dat

leas

ton

cedu

rin

gth

eh

ospi

talv

isit

.

Nu

mbe

rof

un

iqu

en

urs

ing

trea

tmen

tsT

he

nu

mbe

rof

un

iqu

en

urs

ing

trea

tmen

tsde

liver

eddu

rin

gth

eh

ospi

talv

isit

.Cap

ture

du

sin

gN

IC[8

,15]

.

Con

tin

uou

s:a

cou

nt

ofth

eu

niq

ue

nu

rsin

gtr

eatm

ents

deliv

ered

duri

ng

the

hos

pita

lvis

it.

Nu

rsin

gtr

eatm

ents

Any

trea

tmen

tn

urs

ing

pers

onn

elp

erfo

rmed

toen

han

cepa

tien

tou

tcom

es.C

aptu

red

usi

ng

NIC

[8,1

5].

Cat

egor

ical

:(m

ult

ileve

l)[1

6].

(a)

NIC

use

din

>95

%of

visi

ts;d

ivid

edin

toqu

arti

les

1=

1–25

%(l

owes

tu

sera

tes,

incl

ude

s0

use

),2=

26–5

0%qu

arti

le,3

=51

–75%

quar

tile

,4=

76–1

00%

quar

tile

(hig

hes

tu

sera

tes)

.(b

)N

ICu

sed

in≤9

5%an

d>

5%of

visi

ts;d

ivid

edin

toth

irds

:0=

NIC

not

use

d,1=

1–33

%lo

wes

tth

ird,

2=

34–6

7%m

iddl

eth

ird,

3=

68–1

00%

top

thir

d.(c

)N

ICu

sed

in<

5%of

the

visi

ts0=

did

not

rece

ive

the

NIC

,1=

did

rece

ive

the

NIC

.


circumstance that lead to, or could have led to, personalharm. Adverse incidents were collected by the internal inci-dent reporting system at the institution. Adverse incidentsincluded falls, medication errors, procedure-related events(e.g., wrong patient, wrong procedure or test), equipment-related events (e.g., equipment malfunction, unplannedremoval, improper set-up), and new conditions (e.g., skinbreakdown).

4. Analytic Procedures

Due to the large number of study variables, a four-step modelbuilding process using logistic regression was used to answerthe research question.

4.1. Step One. Each independent variable included in theanalysis was tested independently using a bivariate analysisand a Score Statistic to determine the association withoccurrence of an adverse incident. In this bivariate analysis,no other variables were statistically controlled for. Variableswith P values ≤ 0.15 were retained for step two. A P value≤ 0.15 was used as the criterion to guard against eliminatingvariables too soon in this exploratory analysis.

4.2. Step Two. The variables retained in step one (P values ≤0.15) were then analyzed within their respective conceptualvariable blocks (i.e., patient characteristics, clinical condi-tions, context of care, medical treatments, pharmaceuticaltreatments, and nursing treatments) using logistic regres-sion. A backward elimination process was used, indicatingthat the variable with the largest P value was eliminated andthe analysis was rerun on the remaining variables within theblock. This procedure was repeated until all variables withinthe block had a P value ≤ 0.15. A P value of ≤ 0.15 duringstep two was chosen to guard against eliminating variablestoo soon because they might yet prove to have a statisticallysignificant effect when combined with variables from otherconceptual blocks.

4.3. Step Three. A model integrating all of the conceptualvariable blocks was built in a progressive fashion using thevariables that were retained in step two. The significantvariables were added to the model by their respective blocks.Starting with the significant variables in block one (patientcharacteristics) and block 2 (clinical conditions), a modelwas built using the backward elimination process describedin step two until the only variables remaining in the modelwere those with a P value ≤ 0.15. The significant variablesfrom block three (context of care) were then added to whatremained of blocks one (patient characteristics) and two(clinical conditions) in the model. Once again, a backwardelimination process was performed until the only variablesremaining in the model were those with values ≤ 0.15. Thisprocess of adding blocks and using the backward eliminationcontinued until the last block (nursing treatments) wasadded. At this point, when the significant variables fromthe final block were added and backward elimination wasperformed, the criterion for significance was decreased to a Pvalue ≤ 0.05. This resulted in a final model containing only

those variables with a P value ≤ 0.05. In step three, variableswith a P value ≤ 0.05 in the logistic regression indicated thatvariables were significantly related to the dependent variable(occurrence of an adverse incident) after controlling for theother variables in the model.

4.4. Step Four. Covariates used for risk adjustment includedage, severity of illness, and number of hospitalizationsduring the study period (see Table 2). Step four added thesecovariates used for risk adjustment (severity of illness, age,and more than one hospitalization during the study period)to the model to those that were significant in step three.Categorical variables with more than two categories wereanalyzed by comparing each level to a reference category.For example, severity of illness (four levels from minor tosevere) was analyzed by comparing each of the three upperlevel categories to the lowest level of severity of illness (i.e.,minor).

5. Results

There were 10,157 hospitalizations included in this analysis,comprised of 7,851 unique patients. The mean age was 73.7years; most were retired (74.4%), Caucasian (93.5%), female(52.6%), and admitted from home (64.4%). This patientgroup, defined primarily by receiving the nursing treatmentFall Prevention, was medically diverse. The most commonprimary medical diagnoses were diseases of the circulatorysystem (28.5%), neoplasms (13.8%), and injury, includingfractures, or poisoning (11.5%).

There were 1,568 hospitalizations that experienced atleast one adverse incident in this sample. The most com-monly experienced adverse incident for this patient groupincluded medication errors (37%), falls (27%), and equip-ment-related events (14%).

Results of the model building process are illustratedin Table 2 by variable blocks. The bivariate correlationscompleted in step one are not included in Table 2 due tospace constraints but are available from the authors uponrequest. The second column in Table 2 illustrates variablesretained from step one that were analyzed within blockswith P values ≤ 0.15 (step two of model building) and thusretained for step three. The third column includes P valuesfrom the third part of the modeling building process, prior toadding covariates used for risk adjustment to the final model(step four). The final model is illustrated in Table 3.

Five patient characteristics entered step one of the modelbuilding process but none were significant beyond step two.Age, although not significant in any of the three modelbuilding steps, was entered in the final model for riskadjustment [17]. Age was not significant in the final model(see Table 3).

Nine primary medical diagnoses were retained fromstep two, four were retained from step three, and threewere retained (P ≤ 0.05) in the final model (see Tables2 and 3). As the results in Table 3 indicate, other nervoussystem disorders, other primary cancer and senility and organicmental disorders were all significant (P ≤ 0.05) in thefinal model. Other nervous system disorders was the only


Table 2: Results from the model building process for determining explanatory variables of experiencing an adverse incident.

VariableSignificant P values (P ≤ 0.15)for within block correlations

Significant P values (P ≤ 0.05)for the final model

Patient characteristics

Ethnicity 0.0029

Site admitted from <0.0001

Clinical conditions

Primary medical diagnoses (% of sample)

Cancer, other primary (1.7) <0.0001 0.0010

Maintenance chemotherapy, radiotherapy (1.1) 0.1408

Fluid and electrolyte disorder (1.6) 0.0172

Senility & organic mental disorders (3.0) <0.0001 0.0140

Affective (2.1) 0.0007

Other nervous system disorders (1.1) 0.0686 0.0176

Respiratory (3.1) 0.0687

Chronic obstructive pulmonary (1.8) 0.0332

Symptoms, signs, and ill-defined conditions (1.8) 0.1034

Severity of illness <0.0001

Congestive heart failure (11.8) 0.0155

Other neurological disorders (3.6) 0.1218

Diabetes (17.7) 0.0347

Peptic ulcer disease without bleeding (4.4) 0.0985

Rheumatoid arthritis/collagen vas (4.0) 0.0918

Psychoses (5.7) 0.0211

Depression (6.6) 0.0237

Severity of illness

Severity of illness <0.0001

Elixhauser comorbid conditions (% of sample)

Congestive heart failure (11.8) 0.0155

Other neurological disorders (3.6) 0.1218

Diabetes (17.7) 0.0347

Peptic ulcer disease without bleeding (4.4) 0.0985

Rheumatoid arthritis/collagen vas (4.0) 0.0918

Psychoses (5.7) 0.0211

Depression (6.6) 0.0237

Past hospitalizations

Past hospitalizations 0.0199

Context of care variables

Number of units resided on <0.0001

CGPR dip proportion <0.0001 0.0092

Skill mix 0.0003

Average caregiver patient ratio <0.0001 <0.0001

Treatments

Medical treatments

Total number of procedures <0.0001 0.0059

Types of medical treatments (% of sample)

Incision and excision of CNS (2.0) 0.0059

Incision of pleura, thoracentesis, chest drainage (3.8) 0.0637

Coronary artery bypass graft (CABG) (3.1) <0.0001


Table 2: Continued.



Diagnostic cardiac catheterization, coronary arteriography (7.9) 0.0007

Other therapeutic procedures, hemic and lymphatic system (2.8) 0.1205

Upper gastrointestinal endoscopy, biopsy (6.6) 0.0062

Gastrostomy, temporary and permanent (1.5) 0.1035

Oophorectomy, unilateral & bilateral (1.3) 0.0062

Partial excision bone (1.5) 0.0769

Treatment of fracture or dislocation (2.3) 0.0513

Arthroplasty (3.0) 0.0014

Amputation of lower extremity (1.1) 0.1257

Spinal fusion (1.0) 0.0089

Debridement of wound, infection or burn (1.5) 0.0395

Arterio or venogram (not heart or head) (2.2) 0.0091

Diagnostic ultrasound (33.5) 0.0048

Radioisotope scan (6.6) 0.0667

Physical therapy (4.7) <0.0001 0.0015

Psychological and psychiatric evaluation and therapy (1.8) <0.0001

Enteral and parenteral nutrition (9.5) 0.0063

Pharmaceutical treatments

Number of unique medications <0.0001 <0.0001

Types of pharmaceutical treatments (% of sample)

Sympathomimetic (adrenergic) agents (17.3) 0.0241

Anticholinergic agents (13.5) 0.0054

Skeletal muscle relaxants (5.4) 0.0140

Cardiac drugs (64.8) 0.0445

Hypotensive agents (37.7) 0.0882

Psychotherapeutic agents (35.0) <0.0001

Succinimides (27.8) <0.0001 0.0015

Miscellaneous central nervous system agents (3.9) 0.0923

Opiate antagonists (1.4) 0.0669

Anorexigenic agents and respiratory & cerebral stimulants (1.4) 0.0146

Caloric agents (51.8) 0.0244 0.0128

Irrigating solutions (7.3) 0.0414

Ammonia detoxicants (2.7) 0.0785 0.0274

EENT anti-infectives (42.2) 0.0002 0.0148

EENT carbonic anhydrase inhibitors (2.2) 0.0404

Miscellaneous GI drugs (59.8) 0.1098

Parathyroid (1.4) 0.0228

Anti-infectives (21.5) 0.0346

Anti-inflammatory agents (6.8) 0.0438

Multivitamin preparations (18.7) 0.0425

Vitamin B complex (7.4) 0.1130

Unclassified therapeutic agents (34.0) 0.0619

Tetracyclines (1.3) 0.1135

Opiate agonists (64.0) 0.0034

Barbiturates (2.8) 0.0014

Benzodiazepines (56.2) 0.0024


Table 2: Continued.



Misc. anxiolytics, sedatives, & hypnotics (17.8) 0.0022

Nursing treatments

Nursing treatment types (% of sample)

Fluid management (99.5) 0.0098

Bathing (93.5) 0.0600

Pressure ulcer care (91.5) <0.0001 0.0005

Bowel management (88.2) 0.1049

Teaching (81.5) 0.0003

Discharge planning (76.0) 0.0042

Routine care: adult (56.2) 0.0626

Health screening (48.8) <0.0001 <0.0001

Sleep enhancement (47.7) 0.0572

Oxygen therapy (42.4) 0.0008

Post-op care (27.8) <0.0001

Wound care (21.4) 0.0137

Neurologic monitoring (20.2) 0.0002 0.0003

Analgesic administration (17.2) 0.0723

Fluid/electrolyte monitoring (15.1) 0.0365

Medication management (12.2) 0.0678

Nutrition management (11.3) 0.0022

Embolus precautions (9.4) 0.0687

Infection protection (8.9) 0.0182

Enteral tube feeding (9.4) 0.0042

Blood products administration (8.6) 0.0004 0.0192

Restraint (8.5) <0.0001 <0.0001

Postprocedure care (5.6) 0.0219

Specimen management (5.3) 0.0079 0.0098

Active listening (4.8) 0.0161 0.0003

Surgical preparation (4.1) 0.1281 0.0441

Total parenteral nutrition (TPN) administration: adult (3.4) 0.0033

Aspiration precautions (3.2) 0.0233

Anger control assistance (2.8) 0.0177

Mood management (2.5) 0.0091 0.0004

Self-care assistance (2.2) 0.1323

Procedure preparation (2.1) 0.1079

Dementia management (1.6) 0.0816

Electroconvulsive therapy (1.6) 0.0290

Cast care: maintenance (1.1) 0.0035 0.0037

Splinting (1.1) 0.0086

Music therapy (1.1) 0.0036 0.0019

Medical immobilization (0.9) 0.0356

primary medical diagnosis of the three inversely associatedwith experiencing an adverse incident (O.R. = 0.43),indicating that hospitalizations with this medical diagnosiswere less likely to suffer an adverse incident compared tohospitalizations that did not have this condition. Otherprimary cancer and senility and organic mental disorders

were both positively associated with experiencing an adverseincident with odds ratios of 1.94 and 1.57, respectively.

Severity of illness, although not significant in stepthree, was entered into the final model for risk adjustment[17]. Severe and major severity of illness categories weresignificantly (P ≤ 0.05) and positively associated with


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experiencing an adverse incident compared to the lowestseverity of illness category (i.e., mild) (see Table 3).

Seven comorbid conditions were retained from step twofor inclusion in step three but none were significant andthus were not retained for inclusion in the final model.Past hospitalizations during the study period were significantin step two but not in step three (see Table 2). However,this variable was entered into the final model to adjust forpatients that had experienced more than one hospitalizationduring the study period. In the final model (Table 3) pasthospitalizations were not significant.

Four context of care variables, the number of units thepatient resided on during hospitalization, the dip proportion(falling below the unit’s average staffing), skill mix, and theaverage Caregiver Patient Ratio (CGPR) [14], were significantin step two (see Table 2) but only two variables, the dipproportion and average CGPR, were significant in step threeand retained for the final model (see Table 2). Both weresignificant in the final model (step four) as illustrated inTable 3. The average CGPR (RN hours per patient day(HPPDs)) was categorized as quartiles to enable comparisonand interpretation for this nonlinear variable. The twohighest average CGPR quartiles (9.5 RN HPPDs and 6.6RN HPPDs) were significantly (P ≤ 0.05) and inverselyassociated with experiencing an adverse incident, indicatingthat when compared to the lowest quartile of staffing (4.1RN HPPDs), the odds of experiencing an adverse incidentdecreased in the highest two quartiles of nursing hours perpatient day. The odds of experiencing an adverse incidentfor hospitalizations with the highest average CGPR quartile(9.5 RN HPPDs) were 0.76 of the odds for hospitalizationsthat experienced the lowest average CGPR quartile (4.1 RNHPPDs). The odds of experiencing an adverse incident forhospitalizations with the second highest average CGPR (6.6RN HPPDs) were 0.62 of the odds for hospitalizations in thelowest CGPR average quartile.

The CGPR dip proportion was significantly (P = 0.011)and positively associated with experiencing an adverseincident. The results shown in Table 3 are in terms of 0.2increments of change and indicate that for each 20% fallin staffing below the average, the odds of experiencing anadverse incident increase by 15% (O.R. = 1.15).

The number of medical treatments received during hos-pitalization and 20 types of medical treatment were signif-icant in step two (see Table 2) and were therefore includedin step three. In step three of the analysis, the number ofmedical treatments received during hospitalization and onemedical treatment type, physical therapy, were significant(P ≤ 0.05) and retained for the final model. Both werepositively associated with experiencing an adverse incident(see Tables 2 and 3). The results indicate that for eachadditional medical treatment received during hospitaliza-tion, the odds of experiencing an adverse incident increasedby approximately 3% (O.R. = 1.03). Hospitalizations thatreceived the medical treatment physical therapy were 52%(O.R. = 1.52) more likely to experience an adverse incidentthan hospitalizations that did not receive this medicaltreatment.

The number of unique medications received during hos-pitalization and 27 specific pharmaceutical treatments (i.e.,medications types) were significant in step two of the analysis(P ≤ 0.15) and thus retained for step three. The number ofunique medication types and four types of medications weresignificant in step three (see Table 2) and all were significantin the final model (see Table 3). The number of uniquemedications was positively associated (P < 0.001) withexperiencing an adverse incident (O.R. = 1.04). Receipt ofsuccinimides, caloric agents, and EENT anti-infectives duringhospitalization increased the odds of an adverse incident.Ammonia detoxicants were inversely associated (P = 0.021)with experiencing an adverse incident (O.R. = 0.46).

In step two of the analysis, the number of uniquenursing treatments received during hospitalization wasnot significant but 38 types of nursing treatments weresignificant (P ≤ 0.15) and entered into step three (seeTable 2). Eleven were significant at step three and ten weresignificant in the final model (see Tables 2 and 3). Surgicalpreparation was not significant in the final model. Thenursing treatment pressure ulcer care, received by 91.5% ofthe sample, was divided into thirds based on the averagenumber of times per day it was delivered (see Table 1).The results for the three categories of use are interpretedin comparison to hospitalizations that did not receive thenursing treatment. The middle and low use categories ofpressure ulcer care were significantly (P ≤ 0.05) and positivelyassociated with experiencing an adverse incident, indicatingthat hospitalizations that received pressure ulcer care a littleless than once every other day (use rate = 0.41) or once everyfour days (use rate = 0.25) were more likely to experience anadverse incident than hospitalizations that did not receivepressure ulcer care. A similar pattern emerged with thenursing treatment of specimen management. The medium(use rate = 0.34) and low (use rate = 0.10) categorieswere significantly (P ≤ 0.05) and positively associated withexperiencing an adverse incident (see Table 3).

Both health screening and neurologic monitoring hadlow use categories that were significantly (P ≤ 0.05) andpositively correlated with experiencing an adverse incident.The results indicate that hospitalizations that received thelow use of these two nursing treatments were more likely toexperience an adverse incident than hospitalizations that didnot receive the associated nursing treatment (see Table 3).

The medium use category of blood products adminis-tration (use rate = 0.89) was significantly (P ≤ 0.05)and positively (O.R. = 1.49) associated with experiencingan adverse incident. Hospitalizations that received BloodProducts Administration a little less than once a day werealmost 50% more likely to experience an adverse incidentthan hospitalizations that did not receive blood productsadministration.

All three categories of use for the nursing treatmentRestraint were significantly (P < 0.01) and positively associ-ated with experiencing an adverse incident (see Table 3). Thehigh use category had an average delivery of 16.47 times a dayand hospitalizations that received high use of restraint hadmore than double the odds (O.R. = 2.16) of experiencingan adverse incident compared to hospitalizations that did


not receive this nursing treatment. Hospitalizations thatreceived restraint approximately four and a half times aday (medium use category) had almost double the odds(O.R. = 1.86) of experiencing an adverse incident comparedto hospitalizations that did not receive restraint. The lowestcategory of use was delivered an average a little more thanonce a day and increased the likelihood of experiencing anadverse incident by 58% (O.R. = 1.58) compared to no use.

The remaining significant nursing treatments were deliv-ered to less than 5% of the sample and were thereforeoperationalized as dichotomous variables so that hospi-talizations that received the nursing treatment at leastonce are compared to hospitalizations that did not receivethe treatment (see Table 1 for definition). Active listeningreceived at least once by 4.8% of the sample was significantly(P < 0.001) and positively (O.R. = 1.63) associated withexperiencing an adverse incident.

Mood management was received by only 2.5% of thesample but was delivered an average of 3.1 times perday when it was delivered. Hospitalizations that receivedmood management almost doubled their odds (O.R. =1.84) of experiencing an adverse incident compared tohospitalizations that did not receive mood management.

Cast care maintenance was another nursing treatmentthat was delivered frequently (more than five times a dayon average) when hospitalizations required it. Receiving thisnursing treatment doubled the odds (O.R. = 2.00) of experi-encing an adverse incident compared to hospitalizations thatdid not receive this nursing treatment.

Slightly more than one percent of the sample receivedthe nursing treatment music therapy. The average use ratefor hospitalizations that received this treatment was slightlymore than once every ten days (use rate = 0.21). The odds ofexperiencing an adverse incident were double (O.R. = 2.03)for hospitalizations that received this nursing treatmentcompared to hospitalizations that did not receive musictherapy (see Table 3).

6. Discussion

None of the patient characteristics were significant, indicat-ing that patient characteristics were not explanatory variablesof adverse incidents, given the other variables that enteredthe model. Also nonsignificant were two clinical conditions:number of past hospitalizations during the study periodand comorbid medical conditions. This indicates that aftercontrolling for other variables in the model, patient charac-teristics of this sample of older adults were not significant forexperiencing an adverse incident during hospitalization.

Three primary medical diagnoses were significant ex-planatory variables associated with experiencing an adverseincident. Other nervous system disorders were inversely asso-ciated with experiencing an adverse incident. This inverserelationship may be explained by considering the typeof nursing unit these patients are typically admitted to.A primary medical diagnosis of nervous system disorder,which is composed of peripheral and central nervous systemdisorders along with more generic symptoms of a nervoussystem disorder [11], would likely warrant admission to a

neurology unit in this academic medical setting where thenursing personnel are skilled in the care of these patientsand may recognize the need for increased surveillance. Thisheightened surveillance for these specialized patients maydecrease adverse incidents.

Other primary cancer was positively associated withexperiencing an adverse incident. Patients hospitalized withthe primary medical diagnosis of other primary cancer areon high-risk medications, some that call for double-checks,and that may increase the number of medication errors thatare discovered. The third primary medical diagnosis, senilityand organic mental disorders, appears similar in nature toother nervous system disorders but is positively associated withexperiencing an adverse incident, unlike other nervous systemdisorders. This may be because patients who have senilityand organic mental disorders are less capable of using safetyequipment in their environment like call lights and handrails and are more likely to be dispersed among a varietyof general medical or surgical units. The environment andspecialized nursing expertise may not be readily availableto meet the unique care demands of individuals with thisprimary medical condition. In the final model, the top twoseverities of illness categories (i.e., severe and major) weresignificantly and positively associated with experiencing anadverse incident. This is not surprising, as patients who aresicker often have complex care issues which may place themat greater risk to experience an adverse incident.

Related to the structure of care (context of care), the twohighest categories of the average CGPR (RN HPPDs) weresignificantly and inversely associated with experiencing anadverse incident compared to the lowest quartile, indicatingthat when there are more nursing hours per patient day, thereis a decreased likelihood of preventing an adverse incident.This is consistent with findings from previous research [18–24].

The CGPR RN dip proportion was positively associatedwith adverse incidents. The more the RN staffing fell belowthe nursing unit average, the more likely an adverse incidentwas to occur during that hospitalization. This findingindicates that not only is the number of nurses, or HPPDs,an important predictor of adverse incidents but so is staffingbelow the average on a nursing unit. This may indicatethat units develop effective processes dependent upon theiraverage staffing and when the staffing is altered, the processesare impacted. Staffing below the unit average places thepatient at greater risk for having an adverse incident

Processes of care included medical, pharmaceutical, andnursing treatments. Both the number of medical treatmentsand the number of unique medications received duringhospitalization were positively associated with experiencingan adverse incident. As the number of procedures andmedications increased so did the odds of having an adverseincident (e.g., medication error, wrong site surgery, trauma,etc.).

There was one medical treatment, physical therapy, andtwo medication types, succinimides and ammonia detox-icants, that were significantly associated with experienc-ing an adverse incident, which may be related to falls.The positive association between physical therapy and adverse


incidents may be a reflection of patients with decreasedfunctional status who are at greater risk for falling. Similarly,succinimides are anticonvulsives and are in the same AHFSclass as barbiturates and benzodiazepines [9], which arepositively associated with falls [25]. Ammonia detoxicantswas the only pharmaceutical treatment in the final modelinversely associated with experiencing an adverse incident(see Table 3). Patients who require ammonia detoxicantsoften have conditions associated with liver dysfunction,which makes it more difficult for them to excrete ammoniathat builds up in their body. Patients that have high ammonialevels are often confused, disoriented, difficult to direct, andare at great risk for falling for these reasons.

The nursing treatments associated with adverse incidentswere diverse. There was one nursing treatment, pressure ulcercare, that is used to treat an adverse incident (i.e., pressureulcer). There were also a number of nursing treatmentspositively associated with adverse incidents where providingthe treatment showed that the patient likely had greaterexposure to an adverse incident than patients who did notreceive the treatment. One example is the nursing treatmentspecimen management where a patient is more likely to have amislabeled lab as an adverse incident than a patient who didnot receive this treatment. The same could be true for bloodproduct administration and cast care maintenance.

Similarly, all three categories of Restraint were signifi-cantly and positively associated with experiencing an adverseincident. Only 8.5% of the hospitalizations in this samplereceived restraint at least once but the use rates were relativelyhigh, especially the high use category with an average deliveryof 16.47 times per day. These findings also show that use ofrestraints does not prevent adverse incidents (e.g., falls) andin fact may contribute to them as has been demonstrated inother research [26, 27].

Active listening, mood management, and music therapymay be used as complementary therapies for patients whoare distressed, confused, or combative when other treatmentshave not worked. Hospitalizations that require these nursingtreatments may be at greater risk for falling because thepatient is unable to follow commands, is impulsive or unableto communicate effectively.

7. Limitations

This study was conducted at one academic medical centerand therefore further multisite research is needed. Althoughthe effectiveness research model used in this study includesmany important, patient and multidisciplinary components,there were important aspects of care that impact patientsafety such as the individual characteristics of the cliniciansinvolved in care (e.g., experience, education) and how theyinteract with one another (e.g., teamwork, communication)that were not included in this study [28].

8. Conclusion

This study examined a number of patient conditions, struc-tural variables, and process of care variables to better

understand what factors contribute to adverse incidentsduring hospitalization. This is one of the first studies toshow that delivered nursing treatments help explain adverseincidents in hospitalized, older adults. This study also useda multidisciplinary model that considered medical andpharmaceutical components of treatment, which are criticalwhen providing care of the older adult in acute care. Withthis more robust multidisciplinary model, RN staffing wasstill an important explanatory variable for adverse incidents,which is congruent with findings from other research [29,30].

Acknowledgment

This research was supported by a Grant from NIH (PI: Titler.NINR 1 R01 NR05331).

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