Muitas empresas de biopharma atrasam suas contrapartes em outras indústrias na implantação de dados e análises, e a maioria das empresas de biopharma simplesmente não apreciaram as ferramentas. Pior, as empresas de adoção antecipada que tentaram explorar essa fronteira frequentemente descobriram que as soluções turnkey que funcionam em outras indústrias são inadequadas para a biopharma-onde os problemas de negócios se concentram em jornadas específicas de pacientes, exclusivas de áreas e regiões terapêuticas individuais. Para empresas de biopharma, abordagens de adaptação e construção de suas próprias capacidades analíticas são os segredos para o sucesso. Mas a liderança sabia que a empresa não era capaz de identificar toda a população de pacientes que poderiam se beneficiar do medicamento.
A Data-Driven Solution to a Clear Business Challenge
A biopharma company had what sounded like a home run: its leading oncology drug promised to extend a patient’s life expectancy by more than 50% relative to other treatments. But leadership knew that the company wasn’t able to identify the full population of patients who could benefit from the drug.
Diagnosticando o câncer raro que o medicamento tratou exigia um processo difícil e complexo; portanto, a prescrição de decisões não era direta e variou amplamente entre os médicos. Alguns médicos prescreveram o medicamento da empresa, outros prescreveram a oferta de um concorrente e muitos outros optaram por "assistir e esperar" por mais de um ano antes de escrever quaisquer prescrições. Essa variabilidade teve um grande impacto na jornada do paciente; Muitos pacientes que poderiam ter iniciado o tratamento farmacológico anteriormente foram relegados ao grupo de vigilância e espera. Também precisava alcançar oncologistas com as mensagens certas sobre a eficácia do medicamento para informar melhor seu comportamento de prescrição. O resultado foi um ganha-ganha: mais pacientes recebem o tratamento de que precisam, os prestadores de serviços de saúde e os contribuintes podem tomar decisões mais informadas em relação ao tratamento, e a empresa de biopharma tem uma estratégia comercial melhor. no local, focar em seu maior mercado. A equipe conjunta peneirou mais de 10 milhões de registros médicos para 75.000 pacientes, juntamente com dados e informações de reivindicações de pagadores de outras fontes. Para garantir que os dados estivessem sendo interpretados corretamente, a equipe gastou mais de 80 horas entrevistando oncologistas e especialistas em clientes entre funções. diagnosticado com o câncer raro. Os modelos foram projetados para serem preditivos, de aparência prospectiva e repetíveis e baseados em várias fontes de dados, para que a empresa as achasse o mais confiável possível. (Ou seja, eles eram menos propensos a exigir uma dose ou frequência maior, ou um medicamento adicional, posteriormente no tratamento.)
The company needed to identify a broader targeted population of patients who would benefit from the drug. It also needed to reach oncologists with the right messaging about the drug’s effectiveness to better inform their prescribing behavior.
The executive leadership partnered with BCG both to find ways to use real-world data to achieve these goals and to build the company’s capabilities in applying data and analytics to similar challenges in the future. The result was a win-win-win: more patients get the treatment they need, health care providers and payers can make more-informed decisions regarding treatment, and the biopharma company has a better commercial strategy.
Making Sense of the Data
BCG assembled a cross-functional team of data scientists, analysts, physicians, and commercial biopharma experts who worked side-by-side with the client, on site, to focus on its largest market. The joint team sifted through more than 10 million medical records for 75,000 patients, along with payer claims data and information from other sources. To ensure that the data was being interpreted correctly, the team spent more than 80 hours interviewing oncologists and client experts across functions.
Working from that baseline, the joint team developed and tested three machine-learning models that would help the company better understand treatment pathways for patients diagnosed with the rare cancer. The models were designed to be predictive, forward looking, and repeatable and were based on multiple data sources so that the company would find them as reliable as possible.
Through this data-driven approach, the joint team uncovered several critical insights:
- Patients who began treatment with the company’s drug were less likely to “fail” treatment compared with those on a competitor’s drug. (That is, they were less likely to require a greater dosage or frequency, or an additional drug, later in treatment.)
- Adverse side effects varied between patients who took the company’s drug and those who were treated with other medicines.
- Certain oncologists—whom the model could identify—were disproportionally more likely to delay the start of treatment through a watch-and-wait approach.
- Each of the treatment-delaying physicians represented a potential pool of unserved patients who could benefit from the company’s drug—as long as the sales team could reach those physicians with the right messaging.
Translating Insights into Action
Drawing on these insights, the joint team built a digital tool to help sales teams segment physicians more effectively. The tool provided a geographic breakdown of targeted patients, identifying those who were indicated for therapy and were either treated with the competing product or untreated.
The tool also assigned each physician a score based on the size of his or her predicted patient pool and the average duration of watch-and-wait periods. And it included a digital targeting application for the sales team to dynamically identify targeted physicians.
By focusing on the outlier group—those more likely to use that watch-and-wait approach, and to use it for longer periods of time—the team was able to hone the company’s sales message to ensure that key information about the drug’s effectiveness would resonate with targeted physicians.
Achieving Impact
A análise de dados e a segmentação resultante identificaram um pool incremental de mais de 6.000 pacientes qualificados que poderiam potencialmente se beneficiar do medicamento. Recentemente, lançou uma iniciativa para ampliar a ferramenta de segmentação digital para o nível nacional. E a empresa está pronta para usar evidências clínicas do mundo real para melhor atender pacientes com outros produtos em seu portfólio, em outros mercados geográficos.
The project also helped the company team develop its own analytics capability while working side-by-side with the BCG team.
As a result, the company not only can help more patients, it has the ability to replicate that analysis. It recently launched an initiative to scale up the digital targeting tool to the national level. And the company is poised to use real-world clinical evidence to better serve patients with other products in its portfolio, across other geographic markets.
