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Advanced Analytics | forecasting | leukemia | Oncology | patient journey

KMK Patient Journey Analysis Leads Global Pharma Brand To More Accurate Forecasting

A top ten pharmaceutical company contracted with KMK Consulting to provide a patient journey analysis on its recently introduced medication serving a small, rare disease patient population suffering with acute myeloid leukemia (AML). Forecasts based on market research of the patient journey were
not being met, and senior management needed a reset based on a more evidence-based research
approach of the patient journey. KMK was a proven resource to the client, demonstrating successful
engagement with patient level and EMR/EHR data leading to actionable insights.

The Challenge

As a rare disease, the universe of patients suffering from AML is relatively small. Those who would benefit from the client’s treatment with the at-home medication are an even smaller sub-set of the overall patient population with an ever-narrowing patient journey towards treatment. These patients have achieved complete remission after receiving an initial intensive chemotherapy in the hospital, and are not able to continue with intensive curative therapy regardless of blood cell count recovery. The company’s market research had been reliant on physician surveys. However, these could not provide the longitudinal patient views needed which would take into account the impact of market evolutions on patient participation in treatment as well as patient attrition rates along the full patient journey, patient level data and EMR/HER data analysis would be necessary to develop an accurate forecast.

The KMK Solution

The KMK team recognized that this assignment would require establishing the AML “patient journey” to medication eligibility, evaluating the pros and cons of Real-World Data sources available for each qualifying step of the journey, and then leveraging the best data sources for each key indicator to fuel data triangulation that would result in more accurate forecasts.

Once we defined the complete patient journey, we evaluated the Flatiron, COTA, McKesson Electronic Medical Record (EMR) and Electronic Health Record (EHR) data sources in terms of their capabilities, capture rate, source of data, business rule settings, data lag and other client sensitive criteria. Based on this comprehensive review, we then conducted analysis of each step of the patient journey using the data source designated as optimal for that specific phase. 

This patient journey analysis took into account such data as:

• The percentage split of patients in intensive chemotherapy (IC) and non-IC for AML 1st line chemotherapy induction treatment

• The outcome, isolating the percentage of patients achieving complete remission from their first line treatment

• The percentage of AML patients with a specific gene mutation qualifying them for treatment

• The percentage of AML patients who were eligible and who received stem cell transplants

We then performed data triangulation to define patient population percentages in each phase of the journey, leveraging our knowledge of the market and the disease along with a patient flow model to define the percentages. For example, after identifying different trends and spits from different data sources, we were able to test IC/Non-IC split percentages and make a decision on which formula provided the right percentage.

In this manner, we were able to provide forecasts specific to the patient subset likely to be eligible for the client’s treatment modality, and then to build a monthly performance tracker of AML patients within this patient journey.


The forecasting we were able to provide of patients likely to be eligible for the client’s medication based upon the actual patient journey enabled the management team to make decisions on optimizing the brand budget based on viable potential. Prior to our analysis, the company had been working on forecasts significantly overstated. In addition, the monthly performance tracker gives dynamic market insights to the brand team and commercial team, allowing their forecasting team to adjust assumptions during each forecasting cycle in a timely manner.

Unlock Accurate Forecasting with patient Journey Analysis

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