Tash Brusco - Cabrini Health - Factors that Predict Discharge Destination for Patients in a Transition Care Program
 

Tash Brusco - Cabrini Health - Factors that Predict Discharge Destination for Patients in a Transition Care Program

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Tash Brusco delivered the presentation at 2014 Transition Care Conference: Improving Outcomes for Older People. ...

Tash Brusco delivered the presentation at 2014 Transition Care Conference: Improving Outcomes for Older People.

The 2014 Transition Care Conference: Improving Outcomes for Older People formed a National account of the consumers' transition care journey within the current aged care environment, highlighted new initiatives to improve TCP access and quality of care, and showcased innovative service delivery models across jurisdictions.

For more information about the event, please visit: http://www.informa.com.au/transitioncareconference14

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Tash Brusco - Cabrini Health - Factors that Predict Discharge Destination for Patients in a Transition Care Program Tash Brusco - Cabrini Health - Factors that Predict Discharge Destination for Patients in a Transition Care Program Presentation Transcript

  • Factors  to  predict  discharge  des/na/on   in  the   Transi/on  Care  Program   Authors:   Natasha  K  Brusco  1,2,  Nicholas  F  Taylor1,2,  Natalie  A  de  Morton1,3,  Ilana     Hornumg4,  Anna  Smith5,  Kate  Lawler2,  Lauri  Wood6,  Shanandoah  Schaffers7   1,  La  Trobe  University;  2,  Eastern  Health;  3,  Northern  Health;  4,  Western  Health;  5,  Southern  Health;  6,   Echuca  Health;  7,  Peninsula  Health  
  • Acknowledgements   •  The  concept  and  iniIaIon  of  this  project  was  via  the   Victorian  Physiotherapy  TransiIon  Care  Program   Network.  We  acknowledge  the  11  TransiIon  Care   Program  managers  and  Physiotherapy  site  managers   of  the  included  sites  for  the  support  of  their  staff  to   parIcipate  in  the  study.     •  Eastern  Health  Allied  Health  Research  Scholarship   provided  project  lead  hours  over  a  12  month  period   that  greatly  assisted  in  facilitaIng  and  co-­‐ordinaIng   the  project  (NB)  and  La  Trobe  University.    
  • Presen/ng  2  separate  published  papers   •  The  de  Morton  Mobility  Index  (DEMMI)  provides  a   valid  method  for  measuring  and  monitoring  the   mobility  of  paIents  making  the  transiIon  from   hospital  to  the  community:  an  observaIonal  study     •  Natalie  A.  De  Morton,  Natasha  K.  Brusco,  Lauri  Wood,  Katherine  Lawler,  and  Nicholas  F.  Taylor.   "The  de  Morton  Mobility  Index  (DEMMI)  provides  a  valid  method  for  measuring  and   monitoring  the  mobility  of  paIents  making  the  transiIon  from  hospital  to  the  community:  an   observaIonal  study."  Journal  of  physiotherapy  57,  no.  2  (2011):  109-­‐116   •  Factors  that  predict  discharge  desInaIon  for   paIents  in  transiIonal  care:  a  prospecIve   observaIonal  cohort  study     •  Natasha  K.  Brusco,  Nicholas  F.  Taylor,  Ilana  Hornung,  Shanandoah  Schaffers,  Anna  Smith,  and   Natalie  A.  de  Morton.  "Factors  that  predict  discharge  desInaIon  for  paIents  in  transiIonal   care:  a  prospecIve  observaIonal  cohort  study."  Australian  Health  Review  36,  no.  4  (2012):   430-­‐436.  
  • Background  –  Paper  1  The  DEMMI   •  NaIonal  Program  established  2004    /  2005   •  Mandatory  use  of  the  MBI  on  admission  and   discharge  to  the  program   •  de  Morton  Mobility  Index  developed  in  2008   •  Research  QuesIons:   •  Does  the  DEMMI  have  the  properIes  required  to  measure  and   monitor  the  mobility  of  TCP  paIents?   •  Are  DEMMI  scores  valid  when  applied  by  an  allied  health  assistant  to   TCP  paIents?   •  How  does  the  DEMMI  compare  to  the  MBI?    
  • Methods  –  Paper  1  The  DEMMI   •  The  14  TCPs  across  Tasmania  and  Victoria  invited  to   parIcipate   •  Full  Human  Research  Ethics  approval  required   •  MulI-­‐centre  prospecIve  cohort  observaIonal  study   •  Baseline  and  discharge  data  collected   •  6  month  data  collecIon  period  from  October  2009   unIl  April  2010   •  Inclusion  and  exclusion  criteria  applied  
  • Methods  –  Paper  1  The  DEMMI   •  MBI   •  10  items  ordinal  scale  0  –  100   •  Higher  scores  indicate  greater  independence  in   the  domains  of  mobility,  personal  care  and   conInence   •  Well  validated   •  DEMMI   •  Mobility  outcome  measure  recently  developed   •  15  items  interval  level  scale  from  0  –  100   •  8.8  minutes  to  complete  
  • Results  –  Paper  1  The  DEMMI   •  11  of  the  14  sites  were  included     •  696  parIcipants   •  Mean  age  81.9  (SD8.7),  40.5%  male   •  TCP  segng  was  39.3%  hospital  based,  41.3%   residenIal,  20.9%  community  based   •  Mean  LOS  42.3  (SD29.6)   •  Discharge  desInaIon  was  39.0%  home,  14.7%  LLRC,   46.3%  HLRC   •  Change  score  DEMMI  6.4  (13.9),  MBI  4.0  (22.0)  
  • Results  –  Paper  1  The  DEMMI   •  The  DEMMI  and  MBI  are  both  valid  measures  of   acIvity  limitaIon  for  TCP  paIents   •  The  DEMMI  has  a  broader  scale  width  than  the  MBI,   provides  interval  level  measurement  and  is   significantly  more  responsive  to  change  than  the  MBI   for  measuring  the  mobility  of  TCP  paIents   •  DEMMI  items  performed  consistently  regardless  of   whether  a  physiotherapist  or  AHA  administered  the   assessment   •  This  adds  to  the  current  literature  by  introducing  a   valid  measure  of  mobility  for  physiotherapists  in  TCP   that  can  also  be  delegated  for  administered  by  an   AHA,  as  well  as  validaIng  the  current  use  of  the  MBI   within  TCP  
  • Results  –  Paper  1  The  DEMMI  
  • Results  –  Paper  1  The  DEMMI   •  Minimal  clinically  important  difference   •  Analysed  change  in  DEMMI  /  MBI  score  versus  self   reported  change  (self  report  scale  with  “much   bejer”  opIon)   •  DEMMI  change  of  12  is  a  MCID   •  MBI  change  of  13  is  a  MCID  
  • Background  –  Paper  2  Discharge  Predic/on   •  Guidelines  require  low  intensity  allied  health  therapy   •  Research  QuesIons:   •  What  are  the  factors  that  predict  the  discharge   desInaIon  for  paIents  in  the  TCP?   •   What  are  the  factors  that  predict  the  discharge   mobility  and  funcIonal  status  for  paIents  in  the   TCP?   •  What  are  the  factors  that  predict  the  length  of   stay  for  paIents  in  the  TCP?  
  • Methods  –  Paper  2  Discharge  Predic/on   •  Discharge  desInaIon   •  Home   •  Low  Level  ResidenIal  Care   •  High  Level  ResidenIal  Care   •  Other  (acute,  rehabilitaIon,  death)  
  • Methods  –  Paper  2  Discharge  Predic/on   •  Modified  Barthel  Index   •  DEMMI   •  Length  of  Stay   •  Baseline  factors  recorded:   •  Age,  gender,  diagnosis,  Aged  Care  Assessment  Service   classificaIon,  admigng  facility  (acute  facility,  sub-­‐ acute  facility,  community  segng),  TCP  segng,  co-­‐ morbidiIes  as  a  measure  of  the  Charlson  Co-­‐ morbidiIes  Index,  gait  aid  on  admission,  Modified   Barthel  Index  and  DEMMI,  number  of  physiotherapy   sessions,  raIo  of  physiotherapists  to  paIents  in  the   program  
  • Methods  –  Paper  2  Discharge  Predic/on   •  StaIsIcal  analysis   •  Regression  modeling  techniques   •  Baseline  variable  were  used  as  potenIal   covariates   •   The  relaIonship  between  intensity  physiotherapy   staffing  raIo  and  physiotherapy  sessions  received   each  week  was  explored  with  Pearson’s  product   moment  correlaIon  
  • Results  –  Paper  2  Discharge  Predic/on   •  As  per  previous  paper  for  the  paIent  and  program   demographics   •  Physiotherapy  could  be  provided  by  a  physiotherapist   or  an  allied  health  assistant     •  Average  physiotherapy  sessions  per  week  2.0  (SD1.8)   •  Average  staffing  raIo  was  0.06  (SD  0.04)  (~1  physio   to  18  paIents),  with  a  range  of  0.02  (~1  physio  to  50   paIents)  to  0.18  (~1  physio  to  6  paIents)  
  • Results  –  Paper  2  Discharge  Predic/on   •  Primary  outcome   •  PaIents  discharged  home  –  on  admission  LLRC   assessment  and  a  higher  physiotherapy  staffing   raIo     •  PaIents  discharged  to  LLRC  –  on  admission  higher   DEMMI  score,  older  age  and  lower  physiotherapy   staffing  raIo     •  PaIents  discharged  to  HLRC  –  on  admission  HLRC   assessment  and  lower  physiotherapy  staffing  raIo    
  • Results  –  Paper  2  Discharge  Predic/on   •  Secondary  outcomes   •  Higher  discharge  mobility  status  –  on  admission   higher  mobility  status  and  higher  physiotherapy   staffing  raIo   •  Higher  discharge  funcIonal  status  –  on  admission   higher  funcIonal  status  and  higher  physiotherapy   staffing  raIo    
  • Results  –  Paper  2  Discharge  Predic/on   •  Secondary  outcomes  cont.   •  Shorter  length  of  stay  –  on  admission  TCP  in  the   hospital  segng  and  higher  physiotherapy  staffing   raIo   •  The  physiotherapy  staffing  raIo  had  a  Pearsons   correlaIon  of  0.8  with  the  intensity  of   physiotherapy  received  
  • Discussion   •  Supports  validity  of  the  DEMMI  for  measuring  and   monitoring  the  mobility  of  paIents  making  the   transiIon  from  hospital  to  the  community   •  The  moderate  correlaIon  between  the  DEMMI  and   the  MBI  demonstrates  that  they  serve  different   purposes   •  Uni-­‐dimensional  measure  of  mobility  versus  independence   in  acIviIes  of  daily  living   •  Allied  Health  Assistant  versus  Physiotherapist   •  Workforce  implicaIons  
  • Discussion   •  Physiotherapy  staffing  raIo  was  a  consistent  factor   that  predicted  discharge  desInaIon  to  home,  LLRC,   HLRC.   •  This  raIo,  highly  correlated  to  the  number  of   physiotherapy  sessions  received  by  the  paIent   •  Results  of  this  study  does  not  mean  that  the   increased  staff  raIo  caused  the  posiIve  outcomes,   although  this  is  one  possible  explanaIon   •  Results  consistent  with  the  2008  NaIonal  EvaluaIon   of  the  TCP,  higher  Allied  Health  =  bejer  outcomes  
  • Discussion   •  Strengths   •  Large  scale  prospecIve  observaIonal  study   including  11  of  the  14  TCPs  in  Victoria  and   Tasmania   •  This  was  17%  of  the  NaIonal  TCPs  at  the  Ime   •  Similar  characterisIcs  as  those  reported  in  the   2008  NaIonal  EvaluaIon  of  the  TCP   •  Therefore  careful  generalisaIon  is  allowed   •  High  quality,  achieving  6  of  the  7  relevant  criteria   for  an  observaIonal  study  
  • Discussion   •  LimitaIons   •  Care  not  to  infer  causaIon  from  observed   relaIonships   •  Not  an  intervenIon  trial   •  Only  included  paIents  with  ongoing   physiotherapy  intervenIon,  and  those  discharged   within  the  6  month  period   •  Did  not  report  other  factors  that  may  influence   outcomes,  e.g.  social  support  
  • Conclusion   •  The  DEMMI  and  MBI  are  both  valid  measures  of   acIvity  limitaIon  in  the  TCP   •  This  study  has  validated  the  DEMMI  for  measuring   and  monitoring  the  mobility  of  paIents  in  TCP   •  Broad  scale  width  that  captures  the  diverse  range  of   mobility  levels   •  DEMMI  is  more  responsive  to  change  than  the  MBI  
  • Conclusion   •  Factors  on  admission  to  the  TCP  that  predict   discharge  home  include  an  ACAS  classificaIon  of   “home  with  support”  as  well  as  a  higher   physiotherapy  staffing  raIo   •  The  higher  physiotherapy  staffing  raIo  also  predicted   higher  discharge  mobility  and  funcIonal  status   •  A  shorter  LOS    was  predicted  with  a  TCP  segng  in  the   hospital  and  a  higher  physiotherapy  staffing  raIo    
  • Thankyou   Tash  Brusco   Manager  of  Physiotherapy  Services   e:  nbrusco@cabrini.com.au   m:  0408  251  124