Calculating Total Count Based on Time Span

Calculating the total count of doses within a given time span can be a complex task, especially when dealing with overlapping records and different cadence values. In this article, we will explore how to approach this problem using SQL.

Problem Statement

Given a dataset of prescribed doses with start and end dates, along with cadence values, we need to calculate the total count of doses within a given time span. The catch is that records with the same dose and cadence value are considered separate, even if they overlap in date. Additionally, orders for the same dose and cadence value starting on the same day or the next day should be considered as continuing.

Example Data

To illustrate this problem, let’s consider an example dataset:

+---------+---------------+--------+------------+-----------+
| patid  | Generic_Name | Route | s_date    | e_date   |
+---------+---------------+--------+------------+-----------+
| 125     | Haloperidol Decanoate| R      | 2016-07-21| 2016-07-25|
| 125     | Paliperidone Palmitate | O      | 2016-07-21| 2016-07-22 |
| 125     | risperiDONE Consta   | R      | 2016-01-27| 2016-02-16 |
| ...     | ...             | ...    | ...        | ...       |
+---------+---------------+--------+------------+-----------+

Solution

To solve this problem, we can use a combination of SQL techniques, including union operators, window functions, and aggregation.

Step 1: Combine Records with Union Operator

We start by combining all the records into a single list using the UNION ALL operator.

WITH dates AS (
    SELECT *, s_date AS dt, 1 AS direction, ordered_dose AS dose_change
        FROM #prescribed_dose UNION ALL
    -- adjust end dates so they take effect the following day
    SELECT *, DATEADD(day, 1, e_date), -1, -ordered_dose FROM #prescribed_dose
)

This will create a new table dates with all the records combined.

Step 2: Group Records by Patid and Generic Name

Next, we group the records by patient ID (patid) and generic name. We also calculate the cumulative sum of the direction and dose change columns to account for overlapping records.

WITH nodes AS (
    SELECT patid, generic_name, route, dt,
           SUM(direction) AS lvl, SUM(dose_change) AS daily_dose_change
    FROM dates
    GROUP BY patid, generic_name, route, dt
)

This will create a new table nodes with the grouped records.

Step 3: Calculate Cumulative Sums Using Window Functions

We use window functions to calculate the cumulative sum of the direction and dose change columns.

WITH cumulative AS (
    SELECT *,
       SUM(lvl) OVER (PARTITION BY patid, generic_name, route ORDER BY dt) AS total_orders,
       SUM(daily_dose_change) OVER (PARTITION BY patid, generic_name, route ORDER BY dt) AS total_dose,
       LEAD(dt) OVER (PARTITION BY patid, generic_name, route ORDER BY dt) AS next_dt
    FROM nodes
)

This will create a new table cumulative with the cumulative sums.

Step 4: Filter Out Records with No Total Orders

Finally, we filter out records with no total orders to get the final result.

SELECT patid, generic_name, route,
       dt AS s_date, DATEADD(day, -1, next_dt) AS e_date, total_dose, total_orders
FROM cumulative
WHERE total_orders > 0
ORDER BY patid, generic_name, route, dt;

This will give us the final result with the total count of doses within each time span.

Conclusion

Calculating the total count of doses within a given time span can be a complex task, but by using SQL techniques such as union operators, window functions, and aggregation, we can efficiently solve this problem. By following these steps, you should be able to calculate the total count of doses within each time span for your specific dataset.

Last modified on 2023-09-13