using!the!datadriven!policymaking!model!as!a!template!to...

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Stage Three involves analyzing, clarifying and dissemina7on of informa7on. What does the data indicate about the current state of affairs? This por7on of the model includes analyzing the data, clarifying the limita7ons of current knowledge and dissemina7ng the research findings (Weinick & Shin, 2004). Using The DataDriven Policymaking Model As A Template To Aid DNP Students In Analyzing and Evalua7ng Policy O. Danny Lee, PhD, APRNCNS, CNE; Janet Jones, DNS, APRNCNS, & Lisa C. Bayhi, DNP, ACNP, FNP, FAANP Southeastern Louisiana University Introduction Example of The DataDriven Policymaking Model used by a DNP Student to support the DNP Project “Health Policy Analysis of Nurse Prac77oner Ini7a7ves To Develop Advocacy Strategies Towards Full Prac7ce In Louisiana” Purpose To illustrate how the DataDriven Policymaking Model can be used as a template and guide for DNP students in analyzing and evalua7ng policy. Objec7ves Par7cipants will understand and be able to discuss the importance of health care policy for advocacy in health care. Par7cipants will have an understanding of and be able to discuss of the DataDriven Policymaking Model. Par7cipants will understand and be able to discuss the important connec7on between health care policy analysis and advance prac7ce. Par7cipants will understand and be able to discuss the important connec7on between health care policy analysis in suppor7ng evidence base prac7ce. Stage One Stage Two Stage Three Conclusion Stage Four is the ac7on phase and is where policy op7ons, which are supported by the data, are explored. The project is assembled in order to explore specific evidence based alterna7ve policies for NP prac7ce. In addi7on, an es7ma7on of the impact of previous and current ini7a7ves was considered in the prepara7on of the ac7on process. Included in the final product are ways to mi7gate these nega7ve impacts of selected policies and to advance the posi7ve influences (Shamian & ShamianEllen, 2011). This ac7on stage culminates with the produc7on of a white paper that includes the policy analysis and evalua7on with recommenda7ons for policy changes within Louisiana and an algorithmic advocacy plan. Stage Two examines the data available to aid policy development. What data are available to support policy decisions? Assembling a matrix of available data resources and iden7fying the need for new or addi7onal data is necessary in this stage (Shamian & ShamianEllen, 2011). Many types of data resources were explored including state records of legisla7on, acts, board rules and regula7ons as well as stakeholder perspec7ve through state records of mee7ng minutes, speeches, presenta7ons and memos. Na7onal data bases, for example, CDC and AHRQ will be examined for state health and demographic facts. The data resources also include state legislature, statutes, rules and regula7ons, health ranking, expenditures on healthcare, income levels, social structure, overall healthcare ranking and the cost of healthcare in each state. An inventory has been developed of current and past ini7a7ves as well as a determina7on of available measures for implementa7on (Shamian & ShamianEllen, 2011). Stage One includes defini7ons and priori7es and iden7fies the policy problem. Louisiana state statutes do not allow NP prescrip7ve or NP prac7ce without physician oversight. As a consequence, rules and regula7ons have not met the Consensus Model goals, or the IOM ideals. Thus, the policy problem ques7on is what eviden7al changes to prac7ce are required in Louisiana healthcare policy to gain the defini7on of AANP full NP prac7ce? .

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Page 1: Using!The!DataDriven!Policymaking!Model!As!A!Template!To ...dnpconferenceaudio.s3.amazonaws.com/2015... · Stage!Three!involves!analyzing,!clarifying!and! disseminaon!of!informaon.!!!

 Stage  Three  involves  analyzing,  clarifying  and  dissemina7on  of  informa7on.      What  does  the  data  indicate  about  the  current  state  of  affairs?    This  por7on  of  the  model  includes  analyzing  the  data,  clarifying  the  limita7ons  of  current  knowledge  and  dissemina7ng  the  research  findings  (Weinick  &  Shin,  2004).      

Using  The  Data-­‐Driven  Policymaking  Model  As  A  Template  To  Aid  DNP  Students  In  Analyzing  and  Evalua7ng  Policy  O.  Danny  Lee,  PhD,  APRN-­‐CNS,  CNE;  Janet  Jones,  DNS,  APRN-­‐CNS,  &  Lisa  C.  Bayhi,  DNP,  ACNP,  FNP,  FAANP    

Southeastern  Louisiana  University  

Introduction

Example  of  The  Data-­‐Driven  Policymaking  Model  used  by  a  DNP  Student  to  support  the  DNP  Project    “Health  Policy  Analysis  of  Nurse  Prac77oner  Ini7a7ves  To  Develop  Advocacy  Strategies  Towards  Full  Prac7ce  In  Louisiana”    Purpose  To  illustrate  how  the  Data-­‐Driven  Policymaking  Model  can  be  used  as  a  template  and  guide  for  DNP  students  in  analyzing  and  evalua7ng  policy.    Objec7ves  Par7cipants  will  understand  and  be  able  to  discuss  the  importance  of  health  care  policy  for  advocacy  in  health  care.    Par7cipants  will  have  an  understanding  of  and  be  able  to  discuss  of  the  Data-­‐Driven  Policymaking  Model.  Par7cipants  will  understand  and  be  able  to  discuss  the  important  connec7on  between  health  care  policy  analysis  and  advance  prac7ce.  Par7cipants  will  understand  and  be  able  to  discuss  the  important  connec7on  between  health  care  policy  analysis  in  suppor7ng  evidence  base  prac7ce.  

Stage One

Stage Two Stage Three Conclusion

Stage  Four  is  the  ac7on  phase  and  is  where  policy  op7ons,  which  are  supported  by  the  data,  are  explored.      The  project  is  assembled  in  order  to  explore  specific  evidence  based  alterna7ve  policies  for  NP  prac7ce.    In  addi7on,  an  es7ma7on  of  the  impact  of  previous  and  current  ini7a7ves  was  considered  in  the  prepara7on  of  the  ac7on  process.    Included  in  the  final  product  are  ways  to  mi7gate  these  nega7ve  impacts  of  selected  policies  and  to  advance  the  posi7ve  influences  (Shamian  &  Shamian-­‐Ellen,  2011).    This  ac7on  stage  culminates  with  the  produc7on  of  a  white  paper  that  includes  the  policy  analysis  and  evalua7on  with  recommenda7ons  for  policy  changes  within  Louisiana  and  an  algorithmic  advocacy  plan.      

 Stage  Two  examines  the  data  available  to  aid  policy  development.        What  data  are  available  to  support  policy  decisions?    Assembling  a  matrix  of  available  data  resources  and  iden7fying  the  need  for  new  or  addi7onal  data  is  necessary  in  this  stage  (Shamian  &  Shamian-­‐Ellen,  2011).      Many  types  of  data  resources  were  explored  including  state  records  of  legisla7on,  acts,  board  rules  and  regula7ons  as  well  as  stakeholder  perspec7ve  through  state  records  of  mee7ng  minutes,  speeches,  presenta7ons  and  memos.    Na7onal  data  bases,  for  example,  CDC  and  AHRQ  will  be  examined  for  state  health  and  demographic  facts.    The  data  resources  also  include  state  legislature,  statutes,  rules  and  regula7ons,  health  ranking,  expenditures  on  healthcare,  income  levels,  social  structure,  overall  healthcare  ranking  and  the  cost  of  healthcare  in  each  state.    An  inventory  has  been  developed  of  current  and  past  ini7a7ves  as  well  as  a  determina7on  of  available  measures  for  implementa7on  (Shamian  &  Shamian-­‐Ellen,  2011).  

Stage  One  includes  defini7ons  and  priori7es  and  iden7fies  the  policy  problem.  Louisiana  state  statutes  do  not  allow  NP  prescrip7ve  or  NP  prac7ce  without  physician  oversight.    As  a  consequence,  rules  and  regula7ons  have  not  met  the  Consensus  Model  goals,  or  the  IOM  ideals.    Thus,  the  policy  problem  ques7on  is  what  eviden7al  changes  to  prac7ce  are  required  in  Louisiana  healthcare  policy  to  gain  the  defini7on  of  AANP  full  NP  prac7ce?  

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