File size: 8,150 Bytes
5aec14b
aa6bf8a
5aec14b
 
 
aaf8cf2
 
 
 
c37ed45
aa6bf8a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c37ed45
aa6bf8a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
aaf8cf2
5aec14b
c37ed45
5aec14b
 
 
aa6bf8a
5aec14b
 
 
 
aa6bf8a
5aec14b
aaf8cf2
5aec14b
 
c37ed45
aa6bf8a
5aec14b
c37ed45
 
5aec14b
 
aa6bf8a
c37ed45
aa6bf8a
 
c37ed45
 
5aec14b
 
 
 
 
c37ed45
 
 
 
 
 
 
 
 
 
 
aa6bf8a
c37ed45
 
 
 
 
 
 
 
 
5aec14b
c37ed45
5aec14b
 
c37ed45
 
 
5aec14b
 
c37ed45
5aec14b
aa6bf8a
c37ed45
5aec14b
aa6bf8a
 
 
5aec14b
aa6bf8a
 
5aec14b
aaf8cf2
aa6bf8a
 
aaf8cf2
 
aa6bf8a
 
5aec14b
 
 
aa6bf8a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5aec14b
 
 
 
aa6bf8a
5aec14b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
aa6bf8a
5aec14b
 
 
 
 
 
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
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
# utils/oneclick.py
from typing import Tuple, Optional, Dict
from .meldrx import MeldRxAPI
from .responseparser import PatientDataExtractor
from .pdfutils import PDFGenerator
import logging

logger = logging.getLogger(__name__)

def generate_ai_discharge_summary(patient_dict: Dict[str, str], client) -> Optional[str]:
    """Generate a discharge summary using AI based on extracted patient data."""
    try:
        formatted_summary = format_discharge_summary(patient_dict)
        
        logger.info("Generating AI discharge summary with patient info: %s", formatted_summary)

        messages = [
            {
                "role": "assistant",
                "content": (
                    "You are a senior medical practitioner tasked with creating discharge summaries. "
                    "Generate a complete discharge summary based on the provided patient information."
                )
            },
            {"role": "user", "content": formatted_summary}
        ]

        stream = client.chat.completions.create(
            model="grok-3",
            messages=messages,
            temperature=0.4,
            max_tokens=3584,
            top_p=0.7,
            stream=True
        )

        discharge_summary = ""
        for chunk in stream:
            content = chunk.choices[0].delta.content
            if content:
                discharge_summary += content

        logger.info("AI discharge summary generated successfully")
        return discharge_summary.strip()

    except Exception as e:
        logger.error(f"Error generating AI discharge summary: {str(e)}", exc_info=True)
        return None

def generate_discharge_paper_one_click(
    api: MeldRxAPI,
    client,
    patient_id: str = "",
    first_name: str = "",
    last_name: str = ""
) -> Tuple[Optional[str], str, Optional[str], Optional[str]]:
    """
    Generate a discharge summary PDF with one click using MeldRx API data.
    
    Returns:
        Tuple of (pdf_path, status_message, basic_summary, ai_summary)
    """
    try:
        patients_data = api.get_patients()
        if not patients_data or "entry" not in patients_data:
            logger.error("No patient data received from MeldRx API")
            return None, "Failed to fetch patient data from MeldRx API", None, None

        logger.debug(f"Raw patient data from API: {patients_data}")
        
        extractor = PatientDataExtractor(patients_data, "json")
        
        if not extractor.patients:
            logger.error("No patients found in the parsed data")
            return None, "No patients found in the data", None, None

        logger.info(f"Found {len(extractor.patients)} patients in the data")
        
        matching_patients = []
        for i in range(len(extractor.patients)):
            extractor.set_patient_by_index(i)
            patient_data = extractor.get_patient_dict()
            
            patient_id_from_data = patient_data.get('id', '').strip().lower()
            first_name_from_data = patient_data.get('first_name', '').strip().lower()
            last_name_from_data = patient_data.get('last_name', '').strip().lower()
            
            patient_id_input = patient_id.strip().lower()
            first_name_input = first_name.strip().lower()
            last_name_input = last_name.strip().lower()
            
            logger.debug(f"Comparing - ID: {patient_id_input} vs {patient_id_from_data}, "
                        f"First: {first_name_input} vs {first_name_from_data}, "
                        f"Last: {last_name_input} vs {last_name_from_data}")
            
            matches = True
            if patient_id_input and patient_id_from_data != patient_id_input:
                matches = False
            if first_name_input and first_name_from_data != first_name_input:
                matches = False
            if last_name_input and last_name_from_data != last_name_input:
                matches = False
                
            if matches:
                matching_patients.append(patient_data)
                logger.info(f"Found matching patient: {patient_data.get('id', 'unknown')}")

        if not matching_patients:
            search_criteria = f"ID: {patient_id or 'N/A'}, First: {first_name or 'N/A'}, Last: {last_name or 'N/A'}"
            logger.warning(f"No patients matched criteria: {search_criteria}")
            return None, f"No patients found matching criteria: {search_criteria}", None, None
        
        patient_data = matching_patients[0]
        logger.info(f"Selected patient data: {patient_data}")
        
        basic_summary = format_discharge_summary(patient_data)
        ai_summary = generate_ai_discharge_summary(patient_data, client)
        
        if not ai_summary:
            return None, "Failed to generate AI summary", basic_summary, None
            
        pdf_gen = PDFGenerator()
        filename = f"discharge_{patient_data.get('id', 'unknown')}_{patient_data.get('last_name', 'patient')}.pdf"
        pdf_path = pdf_gen.generate_pdf_from_text(ai_summary, filename)
        
        if pdf_path:
            return pdf_path, "Discharge summary generated successfully", basic_summary, ai_summary
        return None, "Failed to generate PDF file", basic_summary, ai_summary

    except Exception as e:
        logger.error(f"Error in one-click discharge generation: {str(e)}", exc_info=True)
        return None, f"Error generating discharge summary: {str(e)}", None, None

def format_discharge_summary(patient_data: dict) -> str:
    """Format patient data into a discharge summary text."""
    patient_data.setdefault('name_prefix', '')
    patient_data.setdefault('first_name', '')
    patient_data.setdefault('last_name', '')
    patient_data.setdefault('dob', 'Unknown')
    patient_data.setdefault('age', 'Unknown')
    patient_data.setdefault('sex', 'Unknown')
    patient_data.setdefault('id', 'Unknown')
    patient_data.setdefault('address', 'Unknown')
    patient_data.setdefault('city', 'Unknown')
    patient_data.setdefault('state', 'Unknown')
    patient_data.setdefault('zip_code', 'Unknown')
    patient_data.setdefault('phone', 'Unknown')
    patient_data.setdefault('admission_date', 'Unknown')
    patient_data.setdefault('discharge_date', 'Unknown')
    patient_data.setdefault('diagnosis', 'Unknown')
    patient_data.setdefault('medications', 'None specified')
    patient_data.setdefault('doctor_first_name', 'Unknown')
    patient_data.setdefault('doctor_last_name', 'Unknown')
    patient_data.setdefault('hospital_name', 'Unknown')
    patient_data.setdefault('doctor_address', 'Unknown')
    patient_data.setdefault('doctor_city', 'Unknown')
    patient_data.setdefault('doctor_state', 'Unknown')
    patient_data.setdefault('doctor_zip', 'Unknown')

    summary = [
        "DISCHARGE SUMMARY",
        "",
        "PATIENT INFORMATION",
        f"Name: {patient_data['name_prefix']} {patient_data['first_name']} {patient_data['last_name']}".strip(),
        f"Date of Birth: {patient_data['dob']}",
        f"Age: {patient_data['age']}",
        f"Gender: {patient_data['sex']}",
        f"Patient ID: {patient_data['id']}",
        "",
        "CONTACT INFORMATION",
        f"Address: {patient_data['address']}",
        f"City: {patient_data['city']}, {patient_data['state']} {patient_data['zip_code']}",
        f"Phone: {patient_data['phone']}",
        "",
        "ADMISSION INFORMATION",
        f"Admission Date: {patient_data['admission_date']}",
        f"Discharge Date: {patient_data['discharge_date']}",
        f"Diagnosis: {patient_data['diagnosis']}",
        "",
        "MEDICATIONS",
        f"{patient_data['medications']}",
        "",
        "PHYSICIAN INFORMATION",
        f"Physician: Dr. {patient_data['doctor_first_name']} {patient_data['doctor_last_name']}".strip(),
        f"Hospital: {patient_data['hospital_name']}",
        f"Address: {patient_data['doctor_address']}",
        f"City: {patient_data['doctor_city']}, {patient_data['doctor_state']} {patient_data['doctor_zip']}",
    ]
    
    return "\n".join(line for line in summary if line.strip() or line == "")