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Degree of EHR Use and Quality of Care Across MN Area Clinics

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Recent …

Recent
  federal
  legislation
  and
  regulations
  have
  resulted
  in
  an
  incentive
  program
  for
  clinics
  to
  implement
  and
  meaningfully
  use
  electronic
  health
  records
  (EHR).
  It
  is
  widely
  believed
  that
  EHRs
  can
  improve
  medical
  care
  by
  providing
  more
  timely
  access
  to
  a
  patient’s
  health
  information,
  facilitating
  the
  tracking
  of
  patients
  over
  time
  to
  ensure
 they
 are
 receiving
 recommended
 care,
 and
 helping
 to
 support
  better
 health
 care
 decisions.
 It
 is
 hoped
 that
 broader
 implementation
  of
  EHRs
  will
  help
  in
  improving
  health
  care
  quality,
  safety,
  and
  efficiency.
 


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  • 1.   Research  Brief   No  1.  August  2010       Degree  of  EHR  Use  and  Quality  of  Care  Across  MN-­Area  Clinics   By  Rebecca  M  Prenevost,  PhD,  MPH     Data   Recent   federal   legislation   and   regulations   have   resulted   in   an   Minnesota   HealthScores   (www.mnhealthscores.org)   incentive   program   for   clinics   to   implement   and   meaningfully   use   was   used   to   obtain   recent   (published   2010)   quality   metrics  for  vascular  and  diabetes  care  as  well  as  EHR   electronic   health   records   (EHR).   It   is   widely   believed   that   EHRs   can   use  metrics  obtained  from  a  2010  health  information   improve   medical   care   by   providing   more   timely   access   to   a   patient’s   technology  (HIT)  ambulatory  clinic  survey.     health   information,   facilitating   the   tracking   of   patients   over   time   to   Health   and   demographic   characteristics   were   ensure  they  are  receiving  recommended  care,  and  helping  to  support   obtained   from   County   Health   Rankings   (http://www.countyhealthrankings.org).   These   data   better  health  care  decisions.  It  is  hoped  that  broader  implementation   were   linked   to   clinics   using   the   primary   care   service   of   EHRs   will   help   in   improving   health   care   quality,   safety,   and   area   (PCSA)   for   the   clinic’s   zip   code   and   the   county   efficiency.     associated  with  that  PCSA.     Measures   To  date,  there  has  been  limited  study  of  the  relationship  between  EHR   6  diabetes  care  and  5  vascular  care  quality  measures   use   and   healthcare   quality,   and   in   studies   that   have   been   published,   were   assessed,   and   7   health/demographic   variables   the   results   have   been   mixed.1-­‐6   To   help   address   this   knowledge   gap,   were   included   in   the   analysis   to   control   for   this  research  brief  analyzes  two  important  publicly-­‐available  datasets   differences  in  patient  populations  (Appendix  A).   published   by   MN   Community   Measurement   along   with   available   EHR   use   was   grouped   into   3   categories.   The   highest   degree  of  use  indicated  the  EHR  was  being  used  1)  for   county  health  statistics  to  assess  the  relationship  between  healthcare   lab/test   results,   2)   to   track   patient   health   problems   quality  and  EHR  use  among  healthcare  clinics  in  the  MN  area.     and  doctor  orders,  and  3)  to  create  benchmarks.  The   lowest   degree   of   use   indicated   clinics   were   not   yet   Study  Findings   using  an  EHR.  Moderate  use  represented  anything  in   There  were  531  clinics  that  reported  EHR  utilization  information  that   between.     also   reported   on   quality   measures   for   either   diabetes   care   (N=514),   Analyses   Descriptive   analyses   were   used   to   examine   average   vascular   care   (N=424),   or   both.   These   clinics   were   spread   across   99   difference   in   quality   scores   by   the   degree   of   EHR   use.   counties  in  four  states  (IA,  MN,  WI,  and  ND),  but  the  vast  majority  of   Multivariate   regression   analyses   were   used   to   clinics  (N=475)  were  located  in  Minnesota.     confirm   whether   the   differences   were   significant   after  controlling  for  population  characteristics.     The   graphs   below   illustrate   the   differences   in   quality   scores   according     to  the  degree  of  EHR  use.  The  red  lines  represent  the  average  percent  of  patients  meeting  the  quality   measure  in  clinics  that  were  non-­‐users  of  EHR.  The  columns  indicate  the  percentage  points  above  this   non-­‐user  benchmark  for  clinics  that  had  implemented  EHRs.  The  darkest  column  represents  the  clinics   with  the  highest  degree  of  EHR  use,  and  the  lighter  column  represents  clinics  with  moderate  EHR  use.   Graph  1:  Average  Difference  in  Diabetes  Quality  for  EHR  Users  Compared  to  Non-­‐User  Benchmark     www.evidity.org     1  
  • 2.   Research  Brief   No  1.  August  2010       Graph  2:  Average  Difference  in  Vascular  Quality  for  EHR  Users  Compared  to  Non-­‐User  Benchmark     Even   after   controlling   for   Table  1:  Regression  Results  Controlling  for  Population  Characteristics   population   characteristics,       Optimal  Diabetes  Care   Optimal  Vascular  Care     most   quality   differences       Coefficient   p-­‐value   Coefficient   p-­‐value   between   EHR   users   and   Highest  Degree  EHR     0.0901   0.000   0.0674   0.000   Moderate  Degree  EHR   0.0637   0.000   0.0527   0.007   non-­‐users   are   statistically   %  Smoking   0.0009   0.644   0.0006   0.768   significant  (see  Appendix  B).     %  Obese   0.0122   0.011   0.0102   0.049   %  Binge  Drinkers   -­‐0.0015   0.456   -­‐0.0021   0.358   The   table   to   the   right   shows   %  Uninsured   -­‐0.0082   0.052   -­‐0.0058   0.219   that   compared   to   clinics   PCP  Rate   -­‐0.0002   0.011   -­‐0.0001   0.183   %  College   0.0044   0.000   0.0055   0.000   that   have   not   yet   %  Unemployed   -­‐0.0082   0.209   -­‐0.0073   0.320   implemented   an   EHR,   an   average  of  9.0%  more  patients  met  all  five  of  the  optimal  diabetes  care  measures  when  seen  at  clinics   that   have   the   highest   degree   of   EHR   use,   and   6.4%   more  met   the   measures   when   seen   at   clinics   that   have  moderate  EHR  use.  Similarly,  an  average  of  6.7%  more  patients  met  all  four  of  the  optimal  vascular   care   measures   when   seen   at   clinics   that   have   the   highest   degree   of   EHR   use,   and   5.3%   more   when   seen   at  clinics  that  have  moderate  EHR  use.     Limitations   There   are   several   limitations   to   consider   when   interpreting   these   results.   First,   the   quality   measures   available  at  the  clinic-­‐level  were  limited  to  2  conditions,  which  represent  only  a   tiny  piece  of  healthcare   quality.   In   addition,   the   control   variables  were   at   the   county-­‐level   and   may   not   accurately   represent   the   actual  patient  populations  obtaining  care  from  the  clinics.  The  analysis  also  does  not  account  for  clinic   characteristics,  such  as  size,  teaching  status,   or  provider  specialties  that  may  affect  quality  scores,  nor   does   it   control   for   selection   bias,   or   the   likelihood   of   a   clinic   with   a   greater   focus   on   quality   to   be   an   early  adopter  of  EHR.  Finally,  these  results  do  not  indicate  whether  the  differences  shown  are  clinically   meaningful.   Specifically,   it   is   unknown   how   differences   in   these   quality   metrics   translate   into   other   downstream   effects,   such   as   fewer   inpatient   stays,   lower   rates   of   complications,   and   reduced   ER   utilization.     Conclusion   Publicly  available  healthcare  quality  and  EHR  utilization  data  show  a  greater  degree  of  EHR  utilization  is   associated   with   higher   quality   scores   for   diabetes   and   vascular   care.   Further   research   should   be   conducted   to   discern   causality   and   determine   whether   other   areas   of   healthcare   quality   have   similar   relationships.   www.evidity.org     2  
  • 3.   Research  Brief   No  1.  August  2010       Appendix  A:  Measure  Definitions   Quality  Measure  Definitions   Blood  Pressure:  The  percentage  of  diabetes  patients,  ages  18-­‐ References   75,   who   maintain   blood   pressure   less   than   130/80.   This   1.   Friedberg   MW,   Coltin   KL,   Safran   DG,   Dresser   M,   measure  is  used  for  diabetes  and  vascular  care.   Zaslavsky   AM,   Schneider   EC.   Associations   between   structural   capabilities   of   primary   care   practices   and   LDL:   The   percentage   of   diabetes   patients,   ages   18-­‐75,   who   performance   on   selected   quality   measures.   Ann   Intern   Med.  2009  Oct  6;151(7):456-­‐63.   lower   LDL   or   "bad"   cholesterol   to   less   than   100   mg/dl.   This   2.   Garrido   T,   Jamieson   L,   Zhou   Y,   Wiesenthal   A,   Liang   L.   measure  is  used  for  diabetes  and  vascular  care.   Effect   of   electronic   health   records   in   ambulatory   care:   retrospective,   serial,   cross   sectional   study.   BMJ.   2005   Non-­‐Smoking:   The   percentage   of   diabetes   patients,   ages   18-­‐ Mar  12;330(7491):581.   75,   who   don’t   smoke.   This   measure   is   used   for   diabetes   and   3.   Linder   JA,   Ma   J,   Bates   DW,   Middleton   B,   Stafford   RS.   vascular  care.   Electronic   health   record   use   and   the   quality   of   ambulatory  care  in  the  United  States.  Arch  Intern  Med.   Aspirin:  The  percentage  of  diabetes  patients,  ages  40-­‐75,  who   2007  Jul  9;167(13):1400-­‐5.   4.  Poon  EG,  Wright  A,  Simon  SR,  Jenter  CA,  Kaushal  R,  Volk   take   an   aspirin   daily.   This   measure   is   used   for   diabetes   and   LA,   Cleary   PD,   Singer   JA,   Tumolo   AZ,   Bates   DW.   vascular  care.   Relationship   between   use   of   electronic   health   record   features   and   health   care   quality:   results   of   a   statewide   HbA1c:  The  percentage  of  diabetes  patients,  ages  40-­‐75,  who   survey.  Med  Care.  2010  Mar;48(3):203-­‐9.   control   blood   sugar   so   that   A1c   level   is   less   than   8%.   This   5.   Welch   WP,   Bazarko   D,   Ritten   K,   Burgess   Y,   Harmon   R,   Sandy   LG.   Electronic   health   records   in   four   community   measure  is  used  for  only  diabetes  care.   physician  practices:  impact  on  quality  and  cost  of  care.  J   Am  Med  Inform  Assoc.  2007  May-­‐Jun;14(3):320-­‐8.     Optimal   Diabetes   Care:   This   measure   shows   the   “D5”,   or   6.  Zhou  L,  Soran  CS,  Jenter  CA,  Volk  LA,  Orav  EJ,  Bates  DW,   percentage   of   diabetes   patients,   ages   18-­‐75,   who   met   all   5   Simon   SR.   The   relationship   between   electronic   health   individual   diabetes   quality   measures:   blood   pressure,   LDL,   record   use   and   quality   of   care   over   time.   J   Am   Med   Inform  Assoc.  2009  Jul-­‐Aug;16(4):457-­‐64.     HbA1c,  non-­‐smoking,  and  aspirin.       Optimal  Vascular  Care:  This  measure  shows  the  percentage  of  diabetes  patients,  ages  18-­‐75,  who  met   all  4  individual  vascular  care  quality  measures:  blood  pressure,  LDL,  non-­‐smoking,  and  aspirin.   Community  Health  Measure  Definitions   %   Smoking:   Percent   of   adults   that   report   smoking   at   least   100   cigarettes   and   that   they   currently   smoke   as  obtained  by  the  Behavioral  Risk  Factor  Surveillance  Survey  (BRFSS).   %  Obese:  Percent  of  adults  that  report  a  BMI  ≥  30  as  obtained  by  BRFSS.   %  Binge  Drinkers:  Percent  of  adults  that  report  binge  drinking  in  the  past  30  days  as  obtained  by  BRFSS.   %  Uninsured:  Percent  of  population  <  age  65  without  health  insurance  as  reported  in  the  Area  Resource   File  (ARF).     PCP  Rate:  Primary  care  provider  rate  per  100Kas  reported  in  the  Area  Resource  File  (ARF).     %   College:   Percent   of   population   age   25+   with   4‑year   college   degree   or   higher   as   obtained   by   the   American  Community  Survey    (ACS).   %  Unemployed:  Percent  of  population  age  16+  unemployed  but  seeking  work  as  reported  by  the  Local   Area  Unemployment  Statistics,  Bureau  of  Labor  Statistics.   www.evidity.org     3  
  • 4.   Research  Brief   No  1.  August  2010       Appendix  B:  Regression  Results        Diabetes  Care   Vascular  Care   BP   Coefficient   p-­‐value*   Coefficient   p-­‐value*   Highest  Level   0.1120   0.000   0.0598   0.001   Moderate  Level   0.1004   0.000   0.0663   0.001   %  Smoking   0.0022   0.344   0.0014   0.513   %  Obese   0.0195   0.001   0.0166   0.001   %  Binge  Drinkers   -­‐0.0024   0.324   -­‐0.0035   0.113   %  Uninsured   -­‐0.0059   0.241   -­‐0.0028   0.541   PCP  Rate   -­‐0.0001   0.160   -­‐0.0001   0.171   %  College   0.0065   0.000   0.0058   0.000   %  Unemployed   -­‐0.0049   0.526   0.0006   0.931   LDL                   Highest  Level   0.1000   0.000   0.0639   0.000   Moderate  Level   0.0548   0.002   0.0300   0.115   %  Smoking   0.0006   0.760   0.0016   0.455   %  Obese   0.0133   0.006   0.0015   0.772   %  Binge  Drinkers   0.0002   0.915   0.0001   0.971   %  Uninsured   -­‐0.0121   0.004   -­‐0.0049   0.288   PCP  Rate   -­‐0.0001   0.292   0.0001   0.528   %  College   0.0024   0.046   0.0033   0.008   %  Unemployed   -­‐0.0132   0.044   -­‐0.0077   0.285   Non-­‐Smoking                   Highest  Level   0.0275   0.006   0.0021   0.860   Moderate  Level   0.0279   0.011   0.0094   0.480   %  Smoking   0.0017   0.181   0.0003   0.842   %  Obese   0.0022   0.462   0.0029   0.403   %  Binge  Drinkers   -­‐0.0015   0.248   -­‐0.0015   0.340   %  Uninsured   0.0019   0.469   0.0027   0.398   PCP  Rate   0.0000   0.793   0.0000   0.867   %  College   0.0013   0.088   0.0027   0.002   %  Unemployed   -­‐0.0217   0.000   -­‐0.0117   0.019   Aspirin                   Highest  Level   0.1238   0.000   0.0312   0.006   Moderate  Level   0.0909   0.000   0.0231   0.067   %  Smoking   0.0017   0.391   0.0024   0.089   %  Obese   0.0130   0.008   0.0028   0.409   %  Binge  Drinkers   0.0056   0.008   -­‐0.0001   0.929   %  Uninsured   -­‐0.0124   0.004   -­‐0.0036   0.242   PCP  Rate   -­‐0.0001   0.107   0.0000   0.868   %  College   0.0028   0.020   -­‐0.0009   0.293   %  Unemployed   -­‐0.0076   0.254   -­‐0.0125   0.009   HbA1c               Highest  Level   0.0521   0.000       Moderate  Level   0.0309   0.025       %  Smoking   0.0005   0.736       %  Obese   0.0098   0.010       %  Binge  Drinkers   0.0003   0.838       %  Uninsured   -­‐0.0061   0.067       PCP  Rate   -­‐0.0002   0.019       %  College   0.0018   0.047       %  Unemployed   -­‐0.0007   0.891       *P-­‐Values  of  .05  or  lower  are  considered  statistically  significant,  bolded.   www.evidity.org     4