Spaces:
Sleeping
Sleeping
File size: 4,790 Bytes
2929135 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
import streamlit as st
from typing import Dict, Any, Optional
class MetricsComponent:
def __init__(self):
"""Initialize the metrics component"""
self.default_metrics = {
"patient_flow": {
"occupied_beds": 75,
"total_beds": 100,
"waiting_time": 15,
"discharge_rate": 8
},
"quality": {
"patient_satisfaction": 8.5,
"compliance_rate": 0.95,
"incident_count": 2
},
"staffing": {
"available_staff": {
"doctors": 20,
"nurses": 50,
"specialists": 15
},
"shift_coverage": 0.92
},
"resources": {
"resource_utilization": 0.75,
"critical_supplies": 3,
"equipment_availability": 0.88
}
}
def _render_metric_card(
self,
title: str,
value: Any,
delta: Optional[str] = None,
help_text: Optional[str] = None
):
"""Render a single metric card"""
st.metric(
label=title,
value=value,
delta=delta,
help=help_text
)
def render(self, metrics: Optional[Dict[str, Any]] = None):
"""
Render the metrics dashboard
Args:
metrics: Optional metrics data to display
"""
metrics = metrics or self.default_metrics
st.markdown("### 📊 Operational Metrics Dashboard")
# Create two rows of metrics
row1_cols = st.columns(4)
row2_cols = st.columns(4)
# First row - Key metrics
with row1_cols[0]:
occupancy = (metrics["patient_flow"]["occupied_beds"] /
metrics["patient_flow"]["total_beds"] * 100)
self._render_metric_card(
"Bed Occupancy 🛏️",
f"{occupancy:.1f}%",
"Normal" if occupancy < 85 else "High",
"Current bed occupancy rate across all departments"
)
with row1_cols[1]:
satisfaction = metrics["quality"]["patient_satisfaction"]
self._render_metric_card(
"Patient Satisfaction 😊",
f"{satisfaction}/10",
"↗ +0.5" if satisfaction > 8 else "↘ -0.3",
"Average patient satisfaction score"
)
with row1_cols[2]:
total_staff = sum(metrics["staffing"]["available_staff"].values())
self._render_metric_card(
"Available Staff 👥",
total_staff,
"Optimal" if total_staff > 80 else "Low",
"Total number of available staff across all roles"
)
with row1_cols[3]:
utilization = metrics["resources"]["resource_utilization"] * 100
self._render_metric_card(
"Resource Utilization 📦",
f"{utilization:.1f}%",
"Efficient" if utilization < 80 else "High",
"Current resource utilization rate"
)
# Second row - Additional metrics
with row2_cols[0]:
self._render_metric_card(
"Waiting Time ⏰",
f"{metrics['patient_flow']['waiting_time']} min",
help_text="Average patient waiting time"
)
with row2_cols[1]:
self._render_metric_card(
"Compliance Rate ✅",
f"{metrics['quality']['compliance_rate']*100:.1f}%",
help_text="Current compliance rate with protocols"
)
with row2_cols[2]:
self._render_metric_card(
"Critical Supplies ⚠️",
metrics['resources']['critical_supplies'],
"Action needed" if metrics['resources']['critical_supplies'] > 0 else "All stocked",
"Number of supplies needing immediate attention"
)
with row2_cols[3]:
self._render_metric_card(
"Shift Coverage 📅",
f"{metrics['staffing']['shift_coverage']*100:.1f}%",
help_text="Current shift coverage rate"
)
# Additional visualization if needed
with st.expander("📈 Detailed Metrics Analysis"):
st.markdown("""
### Trend Analysis
- 📈 Patient flow is within normal range
- 📉 Resource utilization shows optimization opportunities
- 📊 Staff distribution is balanced across departments
""") |