The document discusses how larger inpatient bed units can be designed to efficiently use nursing time through optimized flow and reduced walking distances. Two case studies, Summerlin Hospital and Texoma Medical Center, incorporated racetrack unit designs that reduced travel distances and times spent walking compared to national benchmarks. Post-occupancy data found the units' time distributions across tasks and activities, as well as walking distances, were similar to other top performing hospitals, demonstrating how larger bed counts can be achieved without compromising efficiency.

1. Building Smart Without
Compromising Efficiency

2. Presenters
Bill Seed, Staff Vice President,
Universal Health Services
Angela Lee, AIA, ACHA, LEED AP,
Senior Vice President, HKS
Debajyoti Pati, PhD, FIIA, LEED AP,
Vice President and Director of
Research, HKS

3. Acknowledgments
• Summerlin Hospital,
Las Vegas, NV
• Texoma Medical
Center, Denison, TX
• Rapid Modeling
Corporation

4. 50 Foot Rule

6. CAN LARGER INPATIENT BED UNITS AND
EFFICIENT USE OF NURSING TIME GO
HAND IN HAND?
QUESTION

7. Larger Unit Implications
• (+) Staffing efficiency
• (-) Large floor plate
• (-) Distances
between destinations
– (-) Longer walking
distance
– (-) Time spent walking
Acute care units have increased in floor area by 118 percent over the past 20 years (KSA)

8. Implications of Walking
• System efficiency
– Unnecessary walking
= Waste
• Care quality
– Time away from
patients (patient-
centric care)
– Medication errors
• Individual
performance
– Interactions with
workplace stressors
– Alertness, stress,
fatigue

9. Private Vs. Semi-Private

10. Trend Towards Smaller Units

11. CAN LARGER INPATIENT BED UNITS AND
EFFICIENT USE OF NURSING TIME GO
HAND IN HAND?
QUESTION

12. The UHS-HKS Projects
• Guiding principles:
– Proximity of services
– Amenities for care
– Decentralized nurses’
station
– Computer logistics
by focusing on
the efficiency of
flow, one can
focus on patient-
centric care and
supply nurses
everything they
need without
walking long
distances

13. BY OPTIMIZING FLOW AND REDUCING
POTENTIAL WASTE, THE LARGE UNIT
WOULD NOT RESULT IN WALKING
DISTANCES THAT ARE SUBSTANTIALLY
HIGHER THAN NATIONAL BENCHMARK
HYPOTHESIS

14. BY OPTIMIZING FLOW AND REDUCING
POTENTIAL WASTE, HOW NURSES SPEND
THEIR TIME WOULD NOT CONSTITUTE AN
OUTLIER
HYPOTHESIS

15. Texoma Medical Center
Recognized up front,
inpatient strategies can save
time, money and promote
better patient care.

16. Texoma Medical Center
Use a racetrack configuration,
which offers efficiency as well
as easy wayfinding. The
configuration provides the
most efficient perimeter-to-
core ratio and enables direct
corridor sightlines for
caregivers and patients. It also
promotes separation of public
and service areas.

17. Design Inpatient Unit as a Racetrack Configuration
Provides most efficient perimeter to core ratio.

18. Design Inpatient Unit as a Racetrack Configuration
Enables direct corridor sightlines for caregivers and
patients.

19. Design Inpatient Unit as a Racetrack Configuration
Enables direct corridor sightlines for caregivers and
patients.

20. Design Inpatient Unit as a Racetrack Configuration
Intermediate passages through the core reduce travel
distances.

21. Design Inpatient Unit as a Racetrack Configuration
Lean/efficient double loaded corridors – Easy to Navigate

22. Summerlin Medical Center

23. A Patient Centered Expansion
Pinwheel design will
minimize travel
distances, promote
patient safety, and
maximize views from
patient rooms.

24. A Patient Centered Expansion
New tower will blend
with the existing
hospital in effort to
keep the publicly
recognized hospital
identity.

25. A Patient Centered Expansion
$100 million expansion and renovation in one of
southern Nevada’s fastest growing communities in Las
Vegas, Nevada
All of the changes were made externally, with breaking
through the connecting wall as a final step

26. Summerlin Medical Center

27. Summerlin Medical Center

28. DATA COLLECTION
POST OCCUPANCY PERFORMANCE

29. Time-Motion Data
• Rapid Modeling
Corporation’s
programmed Palm PDAs
• 1 week on each unit
• Summer 2010
• Compared with TCAB
Time Study RN national
database

30. Walking Data
• Sportline pedometer
• 1 week on each unit
• Summer 2010
• Compared with 36-
hospital time-motion
study*
*Hendrich, A., M. Chow, B.A. Skierczynski & Z. Lu. (2008). A 36-Hospital Time and Motion Study: How Do Medical-
Surgical Nurses Spend Their Time? The Permanente Journal, 12(3), 25-34.

31. FINDINGS
POST OCCUPANCY PERFORMANCE

32. Time Data by Task Type
• Value adding:
– Comparison with
TCAB data
Proportion of time spent in value adding tasks: Minimum, Q1, Mean, Q3,
Maximum
34.6%
59.1% 64.2% 68.0%
96.4%
59.8%59.5%
TEXOMASUMMERLIN

33. Time Data by Task Type
• Non value adding
tasks:
– Comparison with
TCAB data
Proportion of time spent in non value adding tasks: Minimum, Q1, Mean, Q3,
Maximum
0%
9.1% 11.5% 13.7%
33.9%
8.2% 10.5%
TEXOMA SUMMERLIN

34. Time Data by Task Type
• Necessary tasks:
– Comparison with
TCAB data
Proportion of time spent in necessary tasks: Minimum, Q1, Mean, Q3, Maximum
2.6%
21.6% 24.2% 27.5%
50.0%
TEXOMASUMMERLIN
32.0%30.0%

35. Time Data by Task Category
• Direct Care Time:
– Comparison with
TCAB data
Proportion of time spent in direct care: Minimum, Q1, Mean, Q3, Maximum
47.8%43.0%
91.5%15.7%
43.2% 48.3% 51.6%
TEXOMASUMMERLIN

36. Time Data by Task Category
• Indirect care time:
– Comparison with
TCAB data
Proportion of time spent in indirect care : Minimum, Q1, Mean, Q3, Maximum
1.7%
13.6% 17.0% 20.1%
37.0%
12.0% 16.5%
TEXOMA SUMMERLIN

37. Time Data by Task Category
• Documentation:
– Comparison with
TCAB data
Proportion of time spent in documentation: Minimum, Q1, Mean, Q3, Maximum
1.6%
15.1% 18.0% 21.1%
38.5%
15.8%10.5%
TEXOMASUMMERLIN

38. Time Data by Task Category
• Administration:
– Comparison with
TCAB data
0% 19.2%
TEXOMA SUMMERLIN
16.0% 19.4%
3.2%5.2% 6.5%
Proportion of time spent in administration: Minimum, Q1, Mean, Q3, Maximum

39. Time Data by Task Category
• Personal:
– Comparison with
TCAB data
Proportion of time spent in personal work: Minimum, Q1, Mean, Q3, Maximum
0%
3.4% 4.5% 5.4%
16.9%
1.0% 2.1%
TEXOMA SUMMERLIN

40. Time Data by Task Category
• Waste:
– Comparison with
TCAB data
Proportion of time wasted: Minimum, Q1, Mean, Q3, Maximum
0%
1.9% 2.9% 3.5%
9.8%
3.0%1.7%
TEXOMASUMMERLIN

41. Time Data by Task Level
• Nurse station:
– Comparison with
TCAB data
Proportion of time spent in nurse station: Minimum, Q1, Mean, Q3, Maximum
0%
27.8% 36.1% 44.2%
89.9%
54.9%47.3%
TEXOMASUMMERLIN

42. Time Data by Task Level
• Patient room:
– Comparison with
TCAB data
Proportion of time spent in patient room: Minimum, Q1, Mean, Q3, Maximum
3.8%
34.6% 40.7% 44.7%
80.7%
37.2%36.3%
TEXOMASUMMERLIN

43. Time Data by Task Level
• Medication:
– Comparison with
TCAB data
Proportion of time spent in medication: Minimum, Q1, Mean, Q3, Maximum
0%
13.4% 16.8% 20.6%
45.6%
15.8% 17.3%
TEXOMA SUMMERLIN

44. Time Data by Task Level
• Off the unit:
– Comparison with
TCAB data
Proportion of time spent of the unit : Minimum, Q1, Mean, Q3, Maximum
0%
1.8% 3.3% 4.2%
22.0%
0.3% 0.8%
TEXOMA SUMMERLIN

45. Walking Data
0 mile 1 mile 2 miles 3 miles 4 miles 5 miles
Day Shift Range
36-Hospital
Night Shift Range
36-Hospital
36-Hospital Study Individual Walking Distance Range
Day Shift
Summerlin
Night Shift
Summerlin
Night Shift
Texoma
Day Shift
Texoma

46. CONCLUSION

47. • The unit operations and efficiencies are
similar to the middle 50% of TCAB
participant hospitals.

48. • While retaining efficiencies in time
distribution across activities and walking
distances, the two units successfully
incorporated larger number of beds
while reducing construction costs.

49. • A healthcare provider can significantly
reduce construction costs but operate
with the same efficiencies.

50. • Integrated decision-making with a primary
focus on the efficiencies of flow can be
used to address the seemingly difficult
task of achieving larger inpatient bed
units as well as efficient use of
caregiver time.