HUSTLE AND FLOW

1. HUSTLE AND FLOW

3. Nancy Oakley RN & Tammy Eastwood RN CEN

4. Our Experience With Beta Testing Voice Recognition Software

5. Who Are We?

6. Morehead Memorial Hospital Emergency Department 99 Bed Community Non Profit Hospital Located In Eden, NC 36,000 Visits Annually 41 FTE�s Meditech EDM EHR

7. How Did We End Up Beta Testing Voice Recognition Software? A small, medical software company was looking for a Meditech hospital to beta test and assist in developing their software. Our hospital nursing informatics officer responded.

8. Karen Hunter RN, BSN,MSN Karen Hunter RN BSN MSN obtained her BSN from Georgia Southern University and her Masters in Nursing Informatics from Duke University. She was actively involved in HIMS conferences where she met Johnny Yuen where she shared her dream of revolutionizing nursing documentation with voice technology. Together they founded a small technology company to pursue beta testing this idea.Karen Hunter RN BSN MSN obtained her BSN from Georgia Southern University and her Masters in Nursing Informatics from Duke University. She was actively involved in HIMS conferences where she met Johnny Yuen where she shared her dream of revolutionizing nursing documentation with voice technology. Together they founded a small technology company to pursue beta testing this idea.

9. Johnny Yuen MBA Johnny is a graduate of the Fuqua School of Business at Duke University and had 25 year of software development, system integration and process re-engineering in supply chain and health care space. His knowledge of inventory management with PDAs and scanning devices coupled with Karen�s nursing informatics background made for a partnership in developing voice recognition software for nursing.Johnny is a graduate of the Fuqua School of Business at Duke University and had 25 year of software development, system integration and process re-engineering in supply chain and health care space. His knowledge of inventory management with PDAs and scanning devices coupled with Karen�s nursing informatics background made for a partnership in developing voice recognition software for nursing.

10. The Pitch We serve as beta test site in return for a reduced cost of purchase of hardware and software once beta testing is complete.

11. What Is Voice Recognition Documentation?

12. What Does Voice Recognition Documentation Do? Occurs in real time Data recorded is more accurate Nursing staff is able to multitask Improves nursing workflow In theoryIn theory

13. How Does It Work? Captures data at the Point of Care using voice and visual prompts Interfaces with stationary PC�s and converts voice to text

14. What Are the Advantages of Voice Recognition Documentation? Increases time at the bedside Preventing logging in and out of a PC Handheld device kept you logged in at all times Green Nurses spend up to 30% of their time documenting. Voice recognition could greatly reduce activities that are not directly related to patient care (Wesley, 2009) Green technology decreases paper usage Nurses spend up to 30% of their time documenting. Voice recognition could greatly reduce activities that are not directly related to patient care (Wesley, 2009) Green technology decreases paper usage

15. Improves Patient Satisfaction through more interactivity with nurse at bedside using voice instead of keyboarding What Are the Advantages of Voice Recognition Documentation? Magical words used to activate device caused some surprised looks from patients, abra cadabra and hocus pocus. The words were chosen to use as activation and termination prompts because they are not common language words that would be used to document. Magical words used to activate device caused some surprised looks from patients, abra cadabra and hocus pocus. The words were chosen to use as activation and termination prompts because they are not common language words that would be used to document.

16. Improves Nurse Satisfaction by documenting on the �fly�. Hands free Enhanced multi-tasking What Are the Advantages of Voice Recognition Documentation?

17. Editing/Documenting Through Voice Commands

18. Maximizes Reimbursement of Revenue through accurate documentation of injection and infusion start stop times What Are the Advantages of Voice Recognition Documentation?

19. Injection/Infusion time calculation= Reimbursement Less error with PDA than with PC documentation Pain reassessments more timely with PDA alerts than without Regulatory Documentation Compliance

20. Timely medical decision making through faster notification and alerts through PDA versus PC or tracker How Is Real Time Documentation Supposed to Enhance Workflow in the ED?

21. Prompt patient disposition= improvement in patient throughput How Is Real Time Documentation Supposed To Enhance Workflow in the ED?

22. Improves Productivity through increased efficiency of nursing workflow What Are the Advantages of Voice Recognition Documentation? Cutting down on the 30% documentation timeCutting down on the 30% documentation time

23. Beta Test Site Benefits Customization Staff Input $$$$ Saving

24. Beta Testing: Decision Point Describe the process of becoming a beta test site We signed a contract with the software developer to beta test the product in our ED. Contract agreement included hardware and software and interfaces provided by developer. We provided staff time and feedback. In return, we would receive software at a discount.Describe the process of becoming a beta test site We signed a contract with the software developer to beta test the product in our ED. Contract agreement included hardware and software and interfaces provided by developer. We provided staff time and feedback. In return, we would receive software at a discount.

25. Project Kickoff Core Group of Super Users Began Training and Developing Software in August 2008 Users Were Voice Trained and Trained to Use Hand Held PDA Using Microsoft Voice Recognition software Super Users worked one on one with developers in creating mobile documentation platform based on Meditech EDM for one year Voice training was utilized to train the software to recognize the user�s voice on their sign in to the system.Voice training was utilized to train the software to recognize the user�s voice on their sign in to the system.

26. Hardware Super Users were issued PDA with earpiece and microphone and touch stylus PDA dimensions 4�X 2�X0.6� with RFID Screen Add pictures of Socket Palm Data Assistant Radio Frequency Interface DeviceAdd pictures of Socket Palm Data Assistant Radio Frequency Interface Device

27. Software Proprietary software running on commercial software - Software developer�s program running on Meditech EDM for PDA usage HL7 interface Voice to Connect Activation HL7 interface translates from Meditech into a commercial software and vice versaHL7 interface translates from Meditech into a commercial software and vice versa

28. Decision Making Process Staff Buy In Is Critical A Core Group of Interested Nurses Was Formed Time Commitment Expense/Budget Allowance Administrative Support Physician Tolerance

29. Project Progression Medication Administration and Patient Bracelet ID scanning Treatment Orders Alerts for Medications and Treatments Built In Physical Assessment Module Built Based Upon Existing EDM screen Alerts expanded to include pain reassessment, vital signs reassessment, restraint checks specific to assigned patients Med orders were entered by physician/nurse in Meditech order entry on a PC. Orders transmitted to PDA and picked up for med administration. PDA sounded alert for med order and treatment ordersMed orders were entered by physician/nurse in Meditech order entry on a PC. Orders transmitted to PDA and picked up for med administration. PDA sounded alert for med order and treatment orders

30. Project Progression Charge Nurse screens were developed to allow for decision making regarding staffing and patient flow to match patient and staff skill sets PDA Walkie Talkie feature developed to allow staff to talk mobile to mobile ED tech screens were developed for vital sign and treatment documentation

31. Project Progression Triage screen piloted but unable to flow well with frequent stop/start voice commands that impeded triage process and inability to reconcile medications IV injection and infusion screen developed to capture injection and infusion times accurately

32. Staff Training Eighteen months into the project ED techs were trained to use PDAs for entering vital signs and treatments Staff nurses were trained to use PDAs for assessments, treatments, medication administration and discharge assessment and IV fluid injection/infusion documentation. Talk about how communication with the techs and nurse were enhanced with the PDA�s. Training was required for beta testing.Talk about how communication with the techs and nurse were enhanced with the PDA�s. Training was required for beta testing.

33. Nursing Staff Perspectives Staff speak about their experiences with voice recognition: advantages, disadvantages, benefits, things to considerStaff speak about their experiences with voice recognition: advantages, disadvantages, benefits, things to consider

34. �The Good� Medications documented at scanning SJ Phone Documentation of vital signs and treatments at the bedside Hand held device worked well with stylus Alerts and prompts/regulatory documentation compliance � Pain reassessment compliance scores higher Patient�s were impressed with cutting edge technology of voice and bedside documentation Nancy and Tammy to discussion No need for separate medication bracelet scanner. SJ phone Pro- Call staff instantly for needs. Con-Didn�t work if earpiece was not in. Vital signs could flow and nurse not have to return to a PC and log in interrupting work flow. Optional voice or stylus entry afforded documentation options based on environment such as background too noisy for voice. Alerts made for enhanced documentation for pain reassessments and restraint checks. IV and injection start stop times and routes documented accurately. Voice commands for IV calculations instantaneous.Nancy and Tammy to discussion No need for separate medication bracelet scanner. SJ phone Pro- Call staff instantly for needs. Con-Didn�t work if earpiece was not in. Vital signs could flow and nurse not have to return to a PC and log in interrupting work flow. Optional voice or stylus entry afforded documentation options based on environment such as background too noisy for voice. Alerts made for enhanced documentation for pain reassessments and restraint checks. IV and injection start stop times and routes documented accurately. Voice commands for IV calculations instantaneous.

35. �The Bad� Inability to enter orders on PDA Slow processor speed PC faster than handheld Wired earpiece and microphone hung on stretchers, poles, chairs ,etc. Secondary patient assessment too long and too slow to document Patients thought you were �crazy� when using voice documentation Staff Buy in Many verbal orders in the ED, if MD has not entered them then orders did not cross to PDA. Nurse had to return to computers to enter orders and sign off on PC rather than return to PDA. Many times orders are completed before they are entered into the system so voice recognition/PDA was not able to perform this task. This feature would have been invaluable had it been developed. Screens were slow to turnover when user group increased. The more users the slower the system became. Staff began to gravitate back to PC because of this. Earpieces were not comfortable despite trying multiple types of earpieces. Ear soreness, hearing impaired, clicking noises Also, stethoscope usage prevented earpiece from being utilized consistently. Bluetooth earpieces would have been ideal but developer was unwilling to invest in these types of earpieces. Secondary assessment took 5-10 minutes to complete on PDA. Patient�s were puzzled about what we were doing when voice documenting. Had to screen what was said in front of patients. Not 100% buy-in. When busy PDA was put down and not utilized. Many verbal orders in the ED, if MD has not entered them then orders did not cross to PDA. Nurse had to return to computers to enter orders and sign off on PC rather than return to PDA. Many times orders are completed before they are entered into the system so voice recognition/PDA was not able to perform this task. This feature would have been invaluable had it been developed. Screens were slow to turnover when user group increased. The more users the slower the system became. Staff began to gravitate back to PC because of this. Earpieces were not comfortable despite trying multiple types of earpieces. Ear soreness, hearing impaired, clicking noises Also, stethoscope usage prevented earpiece from being utilized consistently. Bluetooth earpieces would have been ideal but developer was unwilling to invest in these types of earpieces. Secondary assessment took 5-10 minutes to complete on PDA. Patient�s were puzzled about what we were doing when voice documenting. Had to screen what was said in front of patients. Not 100% buy-in. When busy PDA was put down and not utilized.

36. �The Ugly� Voice not easily recognized Background noise a huge factor Working between a PC and hand held was not efficient in the development of the product Potential for medication errors due to slow crossover of medication documentation across interface Bracelet scanning was inconsistent Frequent battery changes Company did not want to develop software further after evaluation summary from staff Voice recognition was a challenge constantly. System failed to recognize voices consistently so staff gravitated to using stylus or PC PDA documentation would crossover to PC but often there were time lapses or failures in crossover. This was significant and was a hazard for medication errors. If all users were using a PDA then medications would immediately show as given on PDA�s but not on PCs, so if a nurse tried to assist in giving meds from the PC platform and medication administration had not crossed over as given then potential for med error or duplication was possible. Med orders signed off on the PC would not crossover to the PDA. This was the downfall of the system. Voice recognition was a challenge constantly. System failed to recognize voices consistently so staff gravitated to using stylus or PC PDA documentation would crossover to PC but often there were time lapses or failures in crossover. This was significant and was a hazard for medication errors. If all users were using a PDA then medications would immediately show as given on PDA�s but not on PCs, so if a nurse tried to assist in giving meds from the PC platform and medication administration had not crossed over as given then potential for med error or duplication was possible. Med orders signed off on the PC would not crossover to the PDA. This was the downfall of the system.

37. Lessons Learned Nursing staff had an opportunity to participate in potentially ground breaking technology revolutionizing nursing documentation. Beta testing in a small super user group may work well but when expanded to a larger test group may not work as well.

38. Lessons Learned Costly � Salary dollars committed to project was approximately $36,000 Benefit of beta testing piqued staff interest in research and increased critical thinking in the workflow process Software developer process flow and nursing flow are not always congruent Concept still has potential if developed in conjunction with healthcare providers rather than non-clinical developers. Technology supports this platform but needs development.

39. Lessons Learned Language barriers combined with lack of clinical experience/knowledge challenged the development process

40. Summary While the concept of voice recognition is a reality in the medical arena it is a greater challenge in the nursing arena. Factors such as the lack of sophisticated noise filtering software prevent voice recognition from being a reality in nursing departments like the Emergency Department. PDA technology is a more likely resource for bedside/real time documentation than voice recognition at this time

41. Questions???

42. References: A new way to document care (2008). Retrieved December 20, 2010 from http://www.healthdatamanagement.com/issues/2008_54/26547-1.html Carter-Wesley, J. Voice Recognition Dictation for Nurses. Journal of Nursing Administration. 2009; 39 (7/8); 310-312.

43. Hamlet, R. (October 1, 2008). A voice recognition solution for nursing documentation. Retrieved 12/20/2010, from http://www.scribd.com/doc/6333073/A-Voice-Recognition-Solution-for-Nursing-Documentation-Ryland-Hamlet Korst L, Alea C, Chamorr T, Aydin C, Gregory K. Nursing Documentation Time During Implementation of an Electronic Medical Record. Journal of Nursing Administration. 2003; 33 (1); 24-30. References:

44. Speech recognition: Accelerating the adoption of electronic medical records. (2008) Retrieved 12/20/2010, from Nuance.com Web site: http://www.himss.org/content/files/EMR_WP1208.pdf References: