Skip to content

Analytics Module

This module calculates NHS performance metrics and creates comprehensive summary statistics for practice level appointment data analysis.

The module implements statistical analysis and metric calculation including: - Temporal analysis of appointment patterns by month and status - Healthcare professional (HCP) type distribution analysis - Appointment mode analysis (face-to-face, telephone, online) - Regional and geographical performance comparisons - Booking time analysis and access metrics - Key NHS performance indicators (DNA rates, completion rates)

Classes:

Name Description
SummarisationStage

Pipeline stage for creating descriptive statistics and summary tables
Notes

All metrics are calculated according to NHS performance monitoring standards and include appropriate statistical measures (sum, mean, count) for different data types. The module generates both detailed breakdowns and high-level key performance indicators suitable for NHS reporting requirements.

Examples:

>>> config = NHSPracticeAnalysisConfig()
>>> stage = SummarisationStage(config)
>>> context = {"combined_data": joined_dataframe}
>>> results = stage.run(context)

SummarisationStage

Bases: PipelineStage

Pipeline stage for creating descriptive statistics and summary tables.

This stage processes the combined appointment data to generate comprehensive statistical summaries and NHS performance metrics suitable for analysis and reporting purposes.

Parameters:

Name Type Description Default
config NHSPracticeAnalysisConfig

Configuration object containing analysis parameters and specifications.

 required

Attributes:

Name Type Description
config NHSPracticeAnalysisConfig

The configuration object passed during initialisation.

Methods:

Name Description
run

Execute the summarisation stage and generate statistical summaries.
Notes

Generated summaries include: - Monthly appointment trends by status (attended, DNA, cancelled) - Healthcare professional type distribution and workload analysis - Appointment mode analysis (face-to-face, telephone, online) - Regional performance comparisons and geographical analysis - Booking time analysis and access pattern evaluation - Key NHS performance indicators and completion rates

All metrics follow NHS performance monitoring standards and include appropriate statistical measures for different analytical purposes.

Examples:

>>> config = NHSPracticeAnalysisConfig()
>>> stage = SummarisationStage(config)
>>> context = {"combined_data": dataframe}
>>> results = stage.run(context)
Source code in practice_level_gp_appointments/analytics.py 
 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
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
class SummarisationStage(PipelineStage):
    """
    Pipeline stage for creating descriptive statistics and summary tables.

    This stage processes the combined appointment data to generate
    comprehensive statistical summaries and NHS performance metrics suitable
    for analysis and reporting purposes.

    Parameters
    ----------
    config : NHSPracticeAnalysisConfig
        Configuration object containing analysis parameters and specifications.

    Attributes
    ----------
    config : NHSPracticeAnalysisConfig
        The configuration object passed during initialisation.

    Methods
    -------
    run(context)
        Execute the summarisation stage and generate statistical summaries.

    Notes
    -----
    Generated summaries include:
    - Monthly appointment trends by status (attended, DNA, cancelled)
    - Healthcare professional type distribution and workload analysis
    - Appointment mode analysis (face-to-face, telephone, online)
    - Regional performance comparisons and geographical analysis
    - Booking time analysis and access pattern evaluation
    - Key NHS performance indicators and completion rates

    All metrics follow NHS performance monitoring standards and include
    appropriate statistical measures for different analytical purposes.

    Examples
    --------
    >>> config = NHSPracticeAnalysisConfig()
    >>> stage = SummarisationStage(config)
    >>> context = {"combined_data": dataframe}
    >>> results = stage.run(context)
    """

    def __init__(self, config: NHSPracticeAnalysisConfig):
        """
        Initialize the summarisation stage.

        Parameters
        ----------
        config : NHSPracticeAnalysisConfig
            Configuration object containing analysis parameters.
        """
        super().__init__(
            inputs="combined_data",
            outputs="summary_statistics",
            name="summarisation",
        )
        self.config = config

    def run(self, context):
        """
        Create descriptive statistics and summary tables.

        This method processes the combined appointment data to generate
        multiple summary tables and key performance indicators for NHS
        practice level analysis.

        Parameters
        ----------
        context : dict
            Pipeline execution context containing combined appointment data.

        Returns
        -------
        dict
            Updated pipeline context containing summary statistics and metrics.

        Notes
        -----
        The method generates seven main summary categories:
        1. Monthly trends by appointment status
        2. Healthcare professional type analysis
        3. Appointment mode temporal analysis
        4. Regional performance summaries
        5. Booking time access analysis
        6. Overall descriptive statistics
        7. Key NHS performance metrics (DNA rates, completion rates)
        """
        combined_data = self._get_input_values(context)[0]
        logger.info("Creating summary statistics...")

        df = combined_data
        summaries = {}

        monthly_summary = (
            df.groupby(["data_month", "appt_status"])["count_of_appointments"]
            .sum()
            .reset_index()
        )
        summaries["monthly_by_status"] = monthly_summary

        hcp_summary = (
            df.groupby("hcp_type")["count_of_appointments"]
            .agg(["sum", "mean", "count"])
            .reset_index()
        )
        hcp_summary.columns = [
            "hcp_type",
            "total_appointments",
            "mean_appointments",
            "number_of_records",
        ]
        summaries["hcp_type_summary"] = hcp_summary

        mode_summary = (
            df.groupby(["appt_mode", "data_month"])["count_of_appointments"]
            .sum()
            .reset_index()
        )
        summaries["mode_by_month"] = mode_summary

        if "region_name" in df.columns:
            regional_summary = (
                df.groupby("region_name")["count_of_appointments"]
                .agg(["sum", "mean"])
                .reset_index()
            )
            regional_summary.columns = [
                "region_name",
                "total_appointments",
                "mean_appointments",
            ]
            summaries["regional_summary"] = regional_summary

        booking_time_summary = (
            df.groupby("time_between_book_and_appt")["count_of_appointments"]
            .sum()
            .reset_index()
        )
        summaries["booking_time_summary"] = booking_time_summary

        numeric_cols = ["count_of_appointments"]
        desc_stats = df[numeric_cols].describe()
        summaries["descriptive_stats"] = desc_stats

        total_appointments = df["count_of_appointments"].sum()

        if "appt_status" in df.columns:
            dna_appointments = df[df["appt_status"] == "DNA"][
                "count_of_appointments"
            ].sum()
            attended_appointments = df[df["appt_status"] == "Attended"][
                "count_of_appointments"
            ].sum()
            dna_rate = (
                dna_appointments / total_appointments
                if total_appointments > 0
                else 0
            )
            completion_rate = (
                attended_appointments / total_appointments
                if total_appointments > 0
                else 0
            )

            metrics_summary = pd.DataFrame(
                {
                    "metric": [
                        "total_appointments",
                        "dna_rate",
                        "completion_rate",
                        "dna_count",
                        "attended_count",
                    ],
                    "value": [
                        total_appointments,
                        dna_rate,
                        completion_rate,
                        dna_appointments,
                        attended_appointments,
                    ],
                }
            )
            summaries["key_metrics"] = metrics_summary

        logger.info(f"Created {len(summaries)} summary tables")
        self._store_outputs(context, summaries)
        return context

__init__(config)

Initialize the summarisation stage.

Parameters:

Name Type Description Default
config NHSPracticeAnalysisConfig

Configuration object containing analysis parameters.

 required
Source code in practice_level_gp_appointments/analytics.py 
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
def __init__(self, config: NHSPracticeAnalysisConfig):
    """
    Initialize the summarisation stage.

    Parameters
    ----------
    config : NHSPracticeAnalysisConfig
        Configuration object containing analysis parameters.
    """
    super().__init__(
        inputs="combined_data",
        outputs="summary_statistics",
        name="summarisation",
    )
    self.config = config

run(context)

Create descriptive statistics and summary tables.

This method processes the combined appointment data to generate multiple summary tables and key performance indicators for NHS practice level analysis.

Parameters:

Name Type Description Default
context dict

Pipeline execution context containing combined appointment data.

 required

Returns:

Type Description
dict

Updated pipeline context containing summary statistics and metrics.
Notes

The method generates seven main summary categories: 1. Monthly trends by appointment status 2. Healthcare professional type analysis 3. Appointment mode temporal analysis 4. Regional performance summaries 5. Booking time access analysis 6. Overall descriptive statistics 7. Key NHS performance metrics (DNA rates, completion rates)

Source code in practice_level_gp_appointments/analytics.py 
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
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
def run(self, context):
    """
    Create descriptive statistics and summary tables.

    This method processes the combined appointment data to generate
    multiple summary tables and key performance indicators for NHS
    practice level analysis.

    Parameters
    ----------
    context : dict
        Pipeline execution context containing combined appointment data.

    Returns
    -------
    dict
        Updated pipeline context containing summary statistics and metrics.

    Notes
    -----
    The method generates seven main summary categories:
    1. Monthly trends by appointment status
    2. Healthcare professional type analysis
    3. Appointment mode temporal analysis
    4. Regional performance summaries
    5. Booking time access analysis
    6. Overall descriptive statistics
    7. Key NHS performance metrics (DNA rates, completion rates)
    """
    combined_data = self._get_input_values(context)[0]
    logger.info("Creating summary statistics...")

    df = combined_data
    summaries = {}

    monthly_summary = (
        df.groupby(["data_month", "appt_status"])["count_of_appointments"]
        .sum()
        .reset_index()
    )
    summaries["monthly_by_status"] = monthly_summary

    hcp_summary = (
        df.groupby("hcp_type")["count_of_appointments"]
        .agg(["sum", "mean", "count"])
        .reset_index()
    )
    hcp_summary.columns = [
        "hcp_type",
        "total_appointments",
        "mean_appointments",
        "number_of_records",
    ]
    summaries["hcp_type_summary"] = hcp_summary

    mode_summary = (
        df.groupby(["appt_mode", "data_month"])["count_of_appointments"]
        .sum()
        .reset_index()
    )
    summaries["mode_by_month"] = mode_summary

    if "region_name" in df.columns:
        regional_summary = (
            df.groupby("region_name")["count_of_appointments"]
            .agg(["sum", "mean"])
            .reset_index()
        )
        regional_summary.columns = [
            "region_name",
            "total_appointments",
            "mean_appointments",
        ]
        summaries["regional_summary"] = regional_summary

    booking_time_summary = (
        df.groupby("time_between_book_and_appt")["count_of_appointments"]
        .sum()
        .reset_index()
    )
    summaries["booking_time_summary"] = booking_time_summary

    numeric_cols = ["count_of_appointments"]
    desc_stats = df[numeric_cols].describe()
    summaries["descriptive_stats"] = desc_stats

    total_appointments = df["count_of_appointments"].sum()

    if "appt_status" in df.columns:
        dna_appointments = df[df["appt_status"] == "DNA"][
            "count_of_appointments"
        ].sum()
        attended_appointments = df[df["appt_status"] == "Attended"][
            "count_of_appointments"
        ].sum()
        dna_rate = (
            dna_appointments / total_appointments
            if total_appointments > 0
            else 0
        )
        completion_rate = (
            attended_appointments / total_appointments
            if total_appointments > 0
            else 0
        )

        metrics_summary = pd.DataFrame(
            {
                "metric": [
                    "total_appointments",
                    "dna_rate",
                    "completion_rate",
                    "dna_count",
                    "attended_count",
                ],
                "value": [
                    total_appointments,
                    dna_rate,
                    completion_rate,
                    dna_appointments,
                    attended_appointments,
                ],
            }
        )
        summaries["key_metrics"] = metrics_summary

    logger.info(f"Created {len(summaries)} summary tables")
    self._store_outputs(context, summaries)
    return context