Clínica nas Nuvens is a Brazilian cloud-based clinic management software designed for healthcare professionals. It provides tools for managing appointments, patients, procedures, and billing, helping clinics streamline their operations and improve patient care.

Configuring Clínica nas Nuvens as a Source

In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the Clínica nas Nuvens option from the list of connectors. Click Next and you’ll be prompted to add your access.

1. Add account access

You’ll need to provide your API credentials to access your Clínica nas Nuvens data. These credentials can be found in your Clínica nas Nuvens account under Menu > Integrações > API. The following configurations are available:

Client ID: The client ID to authenticate against the Clínica nas Nuvens API.
Client Secret: The client secret to authenticate against the Clínica nas Nuvens API.
Token: The token to authenticate against the Clínica nas Nuvens API.
Start Date: The earliest record date to sync. If left empty, the full history is synced.

Once you’re done, click Next.

2. Select streams

Choose which data streams you want to sync. For faster extractions, select only the streams that are relevant to your analysis. You can select entire groups of streams or pick specific ones.

Tip: The stream can be found more easily by typing its name.

Select the streams and click Next.

3. Configure data streams

Customize how you want your data to appear in your catalog. Select the desired layer where the data will be placed, a folder to organize it inside the layer, a name for each table (which will effectively contain the fetched data) and the type of sync.

Layer: choose between the existing layers on your catalog. This is where you will find your new extracted tables as the extraction runs successfully.
Folder: a folder can be created inside the selected layer to group all tables being created from this new data source.
Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix to all tables at once and make this process faster!
Sync Type: FULL_TABLE sync is used for all streams.

Incremental sync is not supported. The Clínica nas Nuvens API does not provide reliable filters by modification date, so all streams use full table sync. Each extraction will fetch the complete current state of the data.

Once you are done configuring, click Next.

4. Configure data source

Describe your data source for easy identification within your organization, not exceeding 140 characters. To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times). Optionally, you can define some additional settings:

Configure Delta Log Retention and determine for how long we should store old states of this table as it gets updated. Read more about this resource here.
Determine when to execute an Additional Full Sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while.

Once you are ready, click Next to finalize the setup.

5. Check your new source

You can view your new source on the Sources page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.

For you to be able to see it on your Catalog, you need at least one successful source run.

Streams and Fields

Below you’ll find all available data streams from Clínica nas Nuvens and their corresponding fields:

Agenda (Appointments)

Stream containing scheduled appointments and their details.

Field	Type	Description
`id`	Integer	Unique identifier for the appointment
`data`	String	Date of the scheduled appointment
`horaInicio`	String	Start time of the appointment
`horaFim`	String	End time of the appointment
`status`	String	Appointment status
`idPaciente`	Integer	ID of the patient
`emailPaciente`	String	Patient’s email address
`telefoneCelularPaciente`	String	Patient’s mobile phone number
`idOrigemPaciente`	Integer	ID of the patient’s origin/source
`idLocalAgenda`	Integer	ID of the schedule location
`idPessoaExecutor`	Integer	ID of the person performing the appointment
`idTipoConsulta`	Integer	ID of the consultation type
`idTipoConvenio`	Integer	ID of the agreement/insurance type
`idRotulo`	Integer	ID of the label
`encaminhamento`	String	Referral information
`observacoes`	String	Observations about the appointment
`urlSalaEspera`	String	URL of the virtual waiting room
`procedimentos`	Array	Array of procedures for the appointment
`procedimentos[].id`	Integer	Procedure ID
`procedimentos[].nome`	String	Procedure name
`procedimentos[].idEspecialidade`	Integer	Specialty ID
`procedimentos[].idTipoProcedimento`	Integer	Procedure type ID
`procedimentos[].idPromocao`	Integer	Promotion ID
`procedimentos[].quantidade`	Integer	Quantity

Pacientes (Patients)

Stream containing patient records and their personal information.

Field	Type	Description
`id`	Integer	Unique identifier for the patient
`nome`	String	Patient’s full name
`nomeSocial`	String	Patient’s social name
`ativo`	Boolean	Indicates whether the patient is active
`cpfcnpj`	String	Patient’s CPF or CNPJ document number
`dataNascimento`	String	Patient’s date of birth
`sexo`	String	Patient’s biological sex
`genero`	String	Patient’s gender
`idNacionalidade`	Integer	ID of the patient’s nationality
`estrangeiro`	Boolean	Indicates if the patient is a foreigner
`numeroControle`	String	Patient’s control number
`numeroIdentificacao`	String	Patient’s identification number
`idOrigem`	Integer	Origin/source identifier
`contato.email`	String	Patient’s email address
`contato.telefoneCelular`	String	Mobile phone number
`contato.telefoneResidencial`	String	Residential phone number
`contato.telefoneComercial`	String	Commercial phone number
`contato.telefoneRecados`	String	Message phone number
`contato.skype`	String	Skype username
`contato.ramalTelefoneResidencial`	String	Residential phone extension
`contato.ramalTelefoneComercial`	String	Commercial phone extension
`contato.ramalTelefoneRecados`	String	Message phone extension
`endereco.rua`	String	Street name
`endereco.numero`	String	Address number
`endereco.complemento`	String	Address complement
`endereco.bairro`	String	Neighborhood
`endereco.cep`	String	ZIP/postal code
`endereco.idCidade`	Integer	City ID (joins with Cidades stream)

Cidades (Cities)

Lookup table containing city information with state details.

Field	Type	Description
`id`	Integer	Unique identifier for the city
`nome`	String	City name
`codIbge`	String	IBGE code (Brazilian official city code)
`estado.id`	Integer	State identifier
`estado.nome`	String	State name
`estado.sigla`	String	State abbreviation (e.g., SP, RJ, MG)

Especialidades (Specialties)

Lookup table containing medical specialties.

Field	Type	Description
`id`	Integer	Unique identifier for the specialty
`nome`	String	Specialty name (e.g., Cardiologia, Dermatologia)

Nacionalidades (Nationalities)

Lookup table containing nationalities.

Field	Type	Description
`id`	Integer	Unique identifier for the nationality
`nacionalidade`	String	Nationality name (e.g., Brasileira, Portuguesa)

Origem (Patient Sources)

Lookup table containing patient origin/source information. Tracks how patients discovered or were referred to the clinic.

Field	Type	Description
`id`	Integer	Unique identifier for the origin
`nome`	String	Origin name (e.g., “Indicação”, “Google”, “Convênio”)
`ativo`	Boolean	Indicates if the origin is active

Agenda Origem (Schedule Origins)

Lookup table containing schedule/appointment origin information.

Field	Type	Description
`id`	Integer	Unique identifier for the schedule origin
`nome`	String	Schedule origin name
`ativo`	Boolean	Indicates if the record is active

Tipo Convênio (Agreement Types)

Lookup table containing agreement/insurance types (convênios).

Field	Type	Description
`id`	Integer	Unique identifier for the agreement type
`nome`	String	Agreement type name
`ativo`	Boolean	Indicates if the agreement type is active
`particular`	Boolean	Indicates if it’s a private/self-pay option
`beneficio`	Boolean	Indicates benefit status

Tipo Procedimento (Procedure Types)

Lookup table containing procedure types with associated specialties.

Field	Type	Description
`id`	Integer	Unique identifier for the procedure type
`nome`	String	Procedure type name
`ativo`	Boolean	Indicates if the procedure type is active
`especialidades`	Array	Array of specialties that can perform this procedure
`especialidades[].id`	Integer	Specialty ID
`especialidades[].nome`	String	Specialty name

Data Model

The following diagram illustrates the relationships between the core data streams in Clínica nas Nuvens. The arrows indicate the join keys that link the different entities, providing a clear overview of the data structure.

Use Cases for Data Analysis

This guide outlines valuable business intelligence use cases when consolidating Clínica nas Nuvens data, along with ready-to-use SQL queries that you can run on Explorer.

1. Appointment Volume Analysis

Track appointment volumes over time to understand clinic capacity and identify trends. Business Value:

Understand daily and weekly appointment patterns
Identify peak hours and days for resource planning
Monitor appointment status distribution

SQL query

WITH appointment_metrics AS (
    SELECT
        DATE(a.data) AS appointment_date,
        DATE_FORMAT(DATE(a.data), '%W') AS day_of_week,
        a.status,
        COUNT(*) AS total_appointments,
        COUNT(DISTINCT a.idPaciente) AS unique_patients
    FROM
        nekt_raw.clinica_nas_nuvens_agenda a
    WHERE
        DATE(a.data) >= CURRENT_DATE - INTERVAL '30' DAY
    GROUP BY
        DATE(a.data),
        DATE_FORMAT(DATE(a.data), '%W'),
        a.status
)
SELECT
    appointment_date,
    day_of_week,
    status,
    total_appointments,
    unique_patients
FROM
    appointment_metrics
ORDER BY
    appointment_date DESC,
    total_appointments DESC

WITH appointment_metrics AS (
    SELECT
        DATE(a.data) AS appointment_date,
        FORMAT_DATE('%A', DATE(a.data)) AS day_of_week,
        a.status,
        COUNT(*) AS total_appointments,
        COUNT(DISTINCT a.idPaciente) AS unique_patients
    FROM
        `nekt_raw.clinica_nas_nuvens_agenda` a
    WHERE
        DATE(a.data) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
    GROUP BY
        1, 2, 3
)
SELECT
    appointment_date,
    day_of_week,
    status,
    total_appointments,
    unique_patients
FROM
    appointment_metrics
ORDER BY
    appointment_date DESC,
    total_appointments DESC

Sample Result

appointment_date	day_of_week	status	total_appointments	unique_patients
2024-11-27	Wednesday	Confirmado	45	42
2024-11-27	Wednesday	Aguardando	12	12
2024-11-27	Wednesday	Cancelado	3	3
2024-11-26	Tuesday	Confirmado	52	48
2024-11-26	Tuesday	Aguardando	8	8

2. Patient Origin Analysis

Understand where your patients are coming from to optimize marketing efforts. Business Value:

Identify most effective patient acquisition channels
Measure ROI of marketing initiatives
Optimize patient acquisition strategy

SQL query

SELECT
    o.nome AS origem,
    COUNT(DISTINCT p.id) AS total_patients,
    COUNT(DISTINCT CASE WHEN p.ativo = true THEN p.id END) AS active_patients,
    ROUND(
        COUNT(DISTINCT CASE WHEN p.ativo = true THEN p.id END) * 100.0 / 
        NULLIF(COUNT(DISTINCT p.id), 0), 
        1
    ) AS active_rate_pct
FROM
    nekt_raw.clinica_nas_nuvens_pacientes p
    LEFT JOIN nekt_raw.clinica_nas_nuvens_origem o ON p.idOrigem = o.id
GROUP BY
    o.nome
ORDER BY
    total_patients DESC

SELECT
    o.nome AS origem,
    COUNT(DISTINCT p.id) AS total_patients,
    COUNT(DISTINCT CASE WHEN p.ativo = true THEN p.id END) AS active_patients,
    ROUND(
        SAFE_DIVIDE(
            COUNT(DISTINCT CASE WHEN p.ativo = true THEN p.id END) * 100.0,
            COUNT(DISTINCT p.id)
        ), 
        1
    ) AS active_rate_pct
FROM
    `nekt_raw.clinica_nas_nuvens_pacientes` p
    LEFT JOIN `nekt_raw.clinica_nas_nuvens_origem` o ON p.idOrigem = o.id
GROUP BY
    o.nome
ORDER BY
    total_patients DESC

Sample Result

origem	total_patients	active_patients	active_rate_pct
Indicação	1,245	1,102	88.5
Google	856	723	84.5
Convênio	634	589	92.9
Instagram	423	367	86.8
Site	312	256	82.1

3. Procedure and Specialty Analysis

Analyze which procedures and specialties are most in demand. Business Value:

Identify most popular procedures
Understand specialty distribution
Plan resource allocation based on demand

SQL query

WITH procedure_data AS (
    SELECT
        p.id AS procedure_id,
        p.nome AS procedure_name,
        p.idEspecialidade AS specialty_id,
        p.quantidade AS quantity,
        a.id AS appointment_id,
        a.data AS appointment_date
    FROM
        nekt_raw.clinica_nas_nuvens_agenda a
        CROSS JOIN UNNEST(a.procedimentos) AS t(p)
    WHERE
        DATE(a.data) >= CURRENT_DATE - INTERVAL '30' DAY
)
SELECT
    e.nome AS specialty,
    tp.nome AS procedure_type,
    COUNT(DISTINCT pd.appointment_id) AS total_appointments,
    SUM(pd.quantity) AS total_procedures
FROM
    procedure_data pd
    LEFT JOIN nekt_raw.clinica_nas_nuvens_especialidades e ON pd.specialty_id = e.id
    LEFT JOIN nekt_raw.clinica_nas_nuvens_tipo_procedimento tp ON pd.procedure_id = tp.id
GROUP BY
    e.nome,
    tp.nome
ORDER BY
    total_appointments DESC

WITH procedure_data AS (
    SELECT
        p.id AS procedure_id,
        p.nome AS procedure_name,
        p.idEspecialidade AS specialty_id,
        p.quantidade AS quantity,
        a.id AS appointment_id,
        a.data AS appointment_date
    FROM
        `nekt_raw.clinica_nas_nuvens_agenda` a,
        UNNEST(a.procedimentos) AS p
    WHERE
        DATE(a.data) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
)
SELECT
    e.nome AS specialty,
    tp.nome AS procedure_type,
    COUNT(DISTINCT pd.appointment_id) AS total_appointments,
    SUM(pd.quantity) AS total_procedures
FROM
    procedure_data pd
    LEFT JOIN `nekt_raw.clinica_nas_nuvens_especialidades` e ON pd.specialty_id = e.id
    LEFT JOIN `nekt_raw.clinica_nas_nuvens_tipo_procedimento` tp ON pd.procedure_id = tp.id
GROUP BY
    e.nome,
    tp.nome
ORDER BY
    total_appointments DESC

Sample Result

specialty	procedure_type	total_appointments	total_procedures
Dermatologia	Consulta	234	234
Cardiologia	Consulta	189	189
Dermatologia	Procedimento Estético	156	312
Oftalmologia	Consulta	145	145
Pediatria	Consulta	123	123

Implementation Notes

Data Quality Considerations

The Agenda stream uses the start_date configuration to filter appointments by date.
Patient records may have null values for optional contact and address fields.
Use the lookup tables (Cidades, Especialidades, etc.) to enrich your analyses with descriptive names.
To ensure data consistency and prevent pagination drift during long-running extractions, the end date (dataFinal) used in the API request filter is cached and locked to the exact date the extraction begins.

Best Practices

Always join with lookup tables to get descriptive names instead of just IDs.
Consider patient privacy when analyzing personal data.

Skills for agents

Download Clínica nas Nuvens skills file

Clínica nas Nuvens connector documentation as plain markdown, for use in AI agent contexts.

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Clínica nas Nuvens as a data source

Configuring Clínica nas Nuvens as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Data Model

Use Cases for Data Analysis

1. Appointment Volume Analysis

2. Patient Origin Analysis

3. Procedure and Specialty Analysis

Implementation Notes

Data Quality Considerations

Best Practices

Skills for agents

Download Clínica nas Nuvens skills file

​Configuring Clínica nas Nuvens as a Source

​1. Add account access

​2. Select streams

​3. Configure data streams

​4. Configure data source

​5. Check your new source

​Streams and Fields

​Data Model

​Use Cases for Data Analysis

​1. Appointment Volume Analysis

​2. Patient Origin Analysis

​3. Procedure and Specialty Analysis

​Implementation Notes

​Data Quality Considerations

​Best Practices

​Skills for agents

Download Clínica nas Nuvens skills file

Configuring Clínica nas Nuvens as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Data Model

Use Cases for Data Analysis

1. Appointment Volume Analysis

2. Patient Origin Analysis

3. Procedure and Specialty Analysis

Implementation Notes

Data Quality Considerations

Best Practices

Skills for agents