Is Your EHR Safe? New Technologies for Auditing

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855.85HIPAA www.compliancygroup.com Industry leading Education Certified Partner Program Please ask questions For todays Slides http://compliancy-group.com/ slides023/ Todays & Past webinars go to: http://compliancy-group.com/ webinar/ Get Involved. #cgwebinar

description

U.S. legislation such as the Affordable Care Act, HIPAA and HITECH outline rules governing the appropriate use of personal health information (PHI). Unfortunately, current technologies do not adequately monitor PHI use. In particular, while electronic medical records (EMR) systems maintain detailed audit logs that record each access to PHI, the logs contain too many accesses for compliance officers to practically monitor, putting PHI at risk. In this talk I will present the explanation-based auditing system, which aims to filter appropriate accesses from the audit log so compliance officers can focus their efforts on suspicious behavior. The underlying premise of the system is that most appropriate accesses to medical records occur for valid clinical or operational reasons in the process of treating a patient, while inappropriate accesses do not. I will discuss how explanations for accesses (1) capture these clinical and operational reasons, (2) can be mined directly from the EMR database, (3) can be enhanced by filling-in frequently missing types of data, and (4) can drastically reduce the auditing burden.

Transcript of Is Your EHR Safe? New Technologies for Auditing

Page 1: Is Your EHR Safe? New Technologies for Auditing

855.85HIPAA  www.compliancygroup.com  

Industry leading Education

Certified Partner Program

•  Please ask questions •  For todays Slides http://compliancy-group.com/slides023/ •  Todays & Past webinars go to: http://compliancy-group.com/webinar/

Get Involved.

#cgwebinar

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ì  

Daniel  Fabbri  Founder  &  CEO  of  Maize  Analy5cs  

Assistant  Professor  at  Vanderbilt  University  

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Electronic  Medical  Records  

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Problem:  Insecure  Data  1.  Open  access  environment  

2.  Millions  of  accesses  per  week  

3.  Pa<ent  care  is  dynamic  

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Regulations    

HIPAA,  HITECH,  and  Affordable  Care  Act  •  Minimal  requirements  to  access  PHI  •  Security  monitoring  requirements  •  Penal<es  and  fines  for  breaches  

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Paper-­‐Bag  Security  

“Nancy,  I’m  not  sure  that’s  what  HIPAA  had  in  mind.”    

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Basic  Security  Mechanisms  

       

       Fine-­‐grained  access  controls  

 

 

       Permission  escala<on            “Are  you  sure  you  want  to  con<nue?”  WARNING  

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Current  Approaches                    Compliance  officers  manually  review  complaints  

       Flag  “suspicious”  types  of  accesses    (i)  Same  last  name,  (ii)  co-­‐workers,  (iii)  neighbors  

 

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Audit  Limitations  ì  Most  accesses  audited  are  appropriate  

ì  Inves<ga<ons  can  take  days  or  weeks  to  complete  

ì  Poten<al  alert  avalanches  (turn  system  off)  

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Objective    

 

Provide  compliance  officers  the  ability  to    

quickly  and  accurately    

find  inappropriate  access  from  audit  logs.  

 

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Observation  

Most  appropriate  accesses  occur  for  valid  clinical  or  opera5onal  reasons.  

 

   “Authorized    access    is    

limited    to    those    with    the      need    to    know    for    purposes    of    pa5ent    care,    billing,    

medical    record    review    and    quality      assurance.”  

University  of  Michigan  Health  System  Screen  Saver  

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Explanation-­‐Based  Auditing  System  (EBAS)  !""#$%&'()*+",%-%.$-/0%123)!435.-6)

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<D)

E)

Filter  accesses  so  there  are  fewer  for  manual  review.  

i  

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Filter  Based  On  Data  Stored  In  The  EMR  

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What  is  an  Explanation?  

2/17/14 Explanation-Based Auditing

127.0.0.1:8000/user_data/explanation/ 1/2

Manage Data Explore Data Manage Edges Manage Explanations Diagnosis Responsibility

CreateExplanations

Mine Explanations

Test Explanations

ExplanationReports

DeleteExplanations

ExplanationsAn explanation captures the the clinical or operator reason for access. Explanations arerepresented as paths connecting the patient whose record is accessed (i.e., Audit Log->Patient ID)to the employee accessing the record (i.e., Audit Log->Employee ID). Paths are constructed bylinking multiple edges together.

7 explanations!

Active Training Frequency Description Explanation Graph

False 0.333 Medication View

True 0.333 Appointment View

Evidence->Audit Log->Employee ID

Evidence->Audit Log->Patient ID

Evidence->Appointment->Patient ID

Evidence->Appointment->Employee ID

True 0.167 RepeatAccess

View

False 0.167 Floor + Floor View

False 0.500 Appointment+Department

View

Explanation-Based Auditing [email protected]

Connec<on  between  the  pa*ent  and  employee  accessing  the  pa<ent’s  record  

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Explanation  Recommendations    

Find  frequently  occurring  explana*ons    Graph  search  problem  

Recommend  explana*ons  to  compliance  officers  

 Approve  correct  explana<ons    Use  to  filter  future  appropriate  accesses  

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Limitations    

Basic  explana<ons  are  effec<ve  for  doctors,  not  suppor<ng  staff  (e.g.,  nurses,  pharmacists,  central  staffing,  etc.)  

     

Appointments  are  made  with  doctors,  not  nurses.    This  lack  of  data  causes  missed  explana5ons  

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Enhance  Explanations  1.  Automa*cally  fill-­‐in  missing  data:  

 Oncologists  treat  cancer  pa5ents    Pediatric  nurses  work  with  pediatric  physicians  

   

   

Pediatric  nurse  

Pediatric  physician  Hospital  Employees  

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Enhance  Explanations  2/17/14 Explanation-Based Auditing

127.0.0.1:8000/user_data/explanation/ 2/2

False 0.500 Medication+Department

View

False 0.167 Icd +DepartmentTo Icd +Department

View

Evidence->Audit Log->Employee ID

Evidence->Audit Log->Patient ID

Employee Info->Department->Info Value

Employee Info->Department->Employee ID

Department to ICD->Department To Icd->icd

Department to ICD->Department To Icd->department

Patient Info->Icd->Patient ID

Patient Info->Icd->Info Value

© Maize Analytics 2014

1.  Automa*cally  fill-­‐in  missing  data:    Oncologists  treat  cancer  pa5ents    Pediatric  nurses  work  with  pediatric  physicians  

 2.  Mine  new  explana*ons:    

   “The  access  occurred  because    Dr.  Dave  is  an  oncologist,    oncologists  treat  cancer    and  Alice  has  cancer”  

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High-­‐Level  Results      

 95%  of  accesses  in  one-­‐week  sample  filtered      with  high  precision  

   Ongoing  trials  at  major  hospitals  to  evaluate  effec<veness  

See  VLDB  2011,  JAMIA  2012  publica<ons  

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Practical  Example  

ì  US  hospital  audited  accesses  for  1  pa<ent  over  a  few  weeks  

ì  500+  accesses  normally  audited  manually  

ì  EBAS  filtered  the  list  down  to  5  for  manual  review  

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Integrated  Analytics  ì  Search  for  outliers,  then  drill  down  with  EBAS  

Analyze  high  usage    employees  

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Deployment    

Many  hospitals  will  not  release  data  to  the  cloud…yet  

 

 

Hospitals  download  VM  and  run  locally!    

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Data  Extraction  How  to  get  data  into  the  audi<ng  system?  

 

   

Repor<ng  System  (e.g.,  Epic’s  Clarity)  

Text  File   All  within  the  hospital  

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Investigation  Management  

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Short  Video  Summary    

 

Pufng  the  pieces  together!  hhps://www.youtube.com/watch?v=gDEcgVwIgSU  

 

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Why  Use  EBAS?  

busy  /  too  many  audits  /  too  much  manual  effort    need  for  automa5on  /  need  for  improved  HIPAA  procedures  

worried  about  OCR  audits  /  want  more  proac5ve  tools  want  published  &  peer-­‐reviewed  technology    looking  for  a  different  approach  to  audi5ng  

 

Email  us  for  faster  HIPAA  audits!  [email protected]  

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Questions?  

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