Risk Reduction in Healthcare (Text Version)

Slide presentation from the AHRQ 2009 conference

On September 14, 2009, Brion Hurley made this presentation at the 2009 Annual Conference. Select to access the PowerPoint® presentation (1.31 MB) (Plugin Software Help).


Slide 1

Risk Reduction in Healthcare

Brion Hurley
Healthcare System Solutions
Lean Six Sigma Black Belt

Slide 2

Mr. Pareto Head courtesy of Quality Progress magazine.

Image: Cartoon showing that most people are not interested in seminars on prevention, rather they want to attend seminars on reaction, since that is where the savings and improvements are more evident.

Slide 3

How do you manage risks today?

  • Option 1: "We don't have any risks".
  • Option 2: "Hopefully, nothing bad happens today" (hopeful thinking, knock on wood).
  • Option 3: "Everybody needs to be careful all the time!"
  • Option 4: "If you make a mistake, we'll fine/discipline/fire you!"
  • Option 5: "We had a meeting and discussed the chance that <insert risk here> could happen, so go communicate to everyone."
  • Option 6: "We brainstormed what could happen, and we took some actions to minimize the chance."
  • Option 7: "We developed a risk assessment of our process, and have an ongoing action plan and cadence to address the highest prioritized risks."

Slide 4

Common Risk Tools

  • Here are some more formal ways of determining risk in your processes
    • Brainstorming.
    • 5 Why's.
    • Fault Tree Analysis.
    • FMEA.
    • Data Analysis.

Slide 5

Brainstorming

Gather data to determine where to start

  • Group ideas into categories
    • Use Fishbone diagram format (Personnel, Processes, Machine, Environment, Measurement, Supplies, etc).

Image: Displaying a fishbone diagram, with the problem at the head, and the categories as the bones of the fish. A comment is added stating "gather data to determine where to start."

Slide 6

5 Why's

  • Ask why At Least 5 times, keep going until root cause (process error) identified

Patient dose changes excessive WHY?
 → Patient INR higher at preferred lab than clinic WHY?
  → Lab and clinic results vary by 0.20 - 0.40 WHY?
   → Lab MNPT values are different WHY?
    → Labs used different normal population groups WHY?
     → Definition of "normal" population not well-defined (Process).

Process Change: All labs will pool data together for a community MNPT value.

Slide 7

Fault Tree Analysis

An image of a Fault Tree Analysis is shown.

Slide 8

FMEA

  • Failure Mode and Effects Analysis
    • Failure mode = the way in which the failure occurs.
      • Implanted device runs out of batteries, wrong prescription given to patient, patient falls down, patient given wrong dose amount, illegible handwriting.
    • Effects = potential consequence or final outcome of the failure mode
      • Adverse or sentinel event, ER visit, surgery, litigation.
      • Slight pain, redness, patient would not know.
  • Various names associated with it
    • Healthcare (HFMEA), Process (PFMEA), Design (DFMEA), Safety/System (SFMEA), etc.

Slide 9

FMEA Format

  • Process Step.
  • Failure Mode.
  • Effect of Failure.
  • Severity Score.
  • Cause of Failure.
  • Occurrence Score.
  • Prevention & Detection Controls.
  • Detection Score.
  • RPN.
  • Actions.

Severity
X
Occurrence
X
Detection
_________

RPN

Slide 10

Example

An FMEA example which shows a table  of risks from an anticoagulation patient management process.

Slide 11

Risk Priority Number

  • Severity X Occurrence X Detection = RPN.
  • Higher the number, higher the risk to the customer (patient).
  • Scoring is relative and somewhat subjective, key is consistency with team.
  • Difficult to compare across processes, organizations, facilities unless teams are the same.

Slide 12

Severity Rankings
RankingEffectProcess FMEA Severity
10Hazardous-no warningmay endanger machine or operator without warning
9Hazardous- w/ warningmay endanger machine or operator with warning
8Very Highmajor disruption in operations (100% scrap)
7Highminor disruption in operations (may require sorting and some scrap)
6Moderateminor disruption in operations (no sorting but some scrap)
5Lowminor disruption in operations (portion may require rework)
4Very Lowminor disruption in operations (some sorting and portion may require rework)
3Minorminor disruption (some rework but little affect on production rate)
2Very Minorminor disruption (minimal affect on production rate)
1NoneNo effect

Slide 13

Occurrence Rankings
RankingEffectFailure RatesPercent DefectiveCpk
10Extremely High> 1 in 250%Cpk < 0.33
9Very High1 in 333%Cpk ~ 0.5
8Very High1 in 810-15%Cpk ~ 0.75
7High1 in 205% 
6Marginal1 in 1001% 
5Marginal1 in 4000.25%Cpk ~ 1
4Unlikely1 in 20000.05% 
3Low1 in 15,0000.007%Cpk > 1.33
2Very Low1 in 150,0000.0007%Cpk > 1.5
1Remote< 1 in 1,500,0000.000007%Cpk > 1.67

Slide 14

Detection Rankings

RankingEffectProcess FMEA Detection
10Absolute uncertaintyNo known process control to detect cause mechanism and subsequent failure.
9Very remote 
8RemoteRemote chance that process control to detect cause mechanism and subsequent failure.
7Very Low 
6LowLow chance that process control to detect cause mechanism and subsequent failure.
5Moderate 
4Moderately High 
3HighHigh chance that process control to detect cause mechanism and subsequent failure.
2Very High 
1Almost CertainCurrent control almost certain to detect cause mechanism and failure mode.

Slide 15

Example

Image: Same FMEA example as before, except highlighting the top 2 risks by RPN, where the highest RPNs equate to the highest risks.

Provide standard questions to all nurses near phone, include in patient education material process changed so copy of all dose changes should be mailed to patients as confirmation.

Slide 16

Prioritize Actions

Choose top 2-3 items to improve

    • Too many will be overwhelming and seem endless (no more than 1 action per person).
    • If risk reduced, work on next highest (continuous improvement).
    • List investigation plan, unless solution is obvious to all
      • More detailed data collection plan.
      • Test out potential solutions (experiment).
      • Further team brainstorming and investigation.

Slide 17

Data Analysis

  • Sometimes data will tell you there is a risk, or will validate how much risk exists.
  • Are labs in Cedar Rapids consistent with one another when measuring INR values?
  • Overall opinions said "YES" - low risk?
  • Develop an experiment to prove it
    • Already exists a tool, called Gage Repeatability & Reproducibility (R&R).

Slide 18

Summary of Gage R&R Study

Graphic shows 10 patients, with one circled, which points to 6 blood vials, then separates out the vials into 2 groups (labs A and B), with 3 vials in each group. The INR results from each of the 3 vials is listed next to them.

Slide 19

Comparison of Labs - INR

PatientAverage INR at Lab AAverage INR at Lab BINR Difference
12.7773.170-0.393
22.1002.320-0.220
32.8873.110-0.223
41.6931.830-0.137
52.9203.160-0.240
61.2671.413-0.147
73.8774.320-0.443
82.0902.240-0.150
92.9933.300-0.307
103.3003.553-0.253
Overall2.5902.842-0.251

Significant Difference in Averages (p-value = 0.000)
Results Exceeded Gage R&R Acceptance Criteria

Slide 20

Are you doing enough?

  • JCAHO Standard LD.5.2 requires facilities to select at least one high-risk process for proactive risk assessment each year.
    • Such selection to be based, in part, on information published periodically by the Joint Commission that identifies the most frequently occurring types of sentinel events and patient safety risk factors (adverse events).
  • New DNV ISO-9000 hospital accreditation will require prevention activity.
  • Never too late to start risk reduction.

Slide 21

Final Notes

  • Risk assessment has a wide spectrum of implementation
    • The more critical the problem, the more structure (tools) and detail required.
    • Prevention requires formal methods and evidence of analysis and action.
  • Most problems are not new, they have been solved or mitigated already
    • Look nationwide, and outside healthcare.
  • Use actual data whenever possible
    • However, not all risks can be quantified.
  • Start simple, then evolve to more complex methods
    • Doesn't have to be complicated, just get started.

Slide 22

Contact

Brion Hurley
Healthcare System Solutions
http://www.healthcaresystemsolutions.com
800-628-9841
sales@healthcaresystemsolutions.com

Current as of December 2009
Internet Citation: Risk Reduction in Healthcare (Text Version). December 2009. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/news/events/conference/2009/hurley/index.html