The economic and social cost of mental illnesses to the economy is estimated at £105 billion a year – roughly the cost of the entire NHS.

When you consider that patients with a severe and prolonged mental illness are almost twice as likely to die from coronary heart disease, four times more likely to die from respiratory disease and are at a higher risk of being overweight or obese, that estimate is likely to be conservative.

Much like every other part of our health service, efficiencies will have to be found if we are to continue to improve mental health services and make them available to those in need.

Reducing clinical risk with data

Risk management is a key component of effective mental health treatment. Mental health services can come into contact with individuals who might present higher risks of suicide, neglect, self-injury, exploitation (physical, financial or sexual) and harm towards others; although this is in no way the case across the board.

As a clinician, a large part of our job is weighing the impact of potential risks holistically, within the context of a broader and complete appraisal of the patient.

Mental health treatment is rarely black and white – as a clinician you must be able to justify why you have chosen a certain course of action over another, and provide a clear rationale for treatment.

A patient’s background history is often complex, requiring data from multiple sources and interactions over extensive periods of time. It may be necessary to review the entire patient record as far back as childhood, in order to look at the full picture and allow you to put current behaviour and triggers into context.

Without access to the full patient history, this can lead to management decisions being taken with a limited understanding of the associated risks, and result in poorer outcomes.

For example, not being able to access a patient’s history of prior episodes of self-harming or even attempts at suicide (due to them not wanting to disclose that information), can result in the implementation of management plans that do not address these risks or appropriately support the patient.

A key component of this is data. Much like physical health, efficient and effective patient diagnosis and treatment is often reliant on having access to a complete, and historical, view of the patient.

Efficiency benefits in the real world

In a mental health outpatient clinic, you will often have a series of  back-to-back appointments, with fixed time slots.

Often, multiple parties will be attending these reviews. During the interview, there’s a number of tasks to complete, including reviewing the patient’s mental state, obtaining collateral histories, completing a medication review (and discussing treatment options), discussing psychological matters and exploring social issues.

But, if you take into account that a complete view of the patient is critical to reducing clinical risk in mental health treatment, that leaves little time to review case history and corroborate your findings.

As a consequence, prior to the face-to-face interview, you can spend a significant proportion of your appointment time solely reviewing a patient’s case history in order to collect data from disparate systems and build it into a coherent history that can be used to assess the patient efficiently.

Although the relevant patient data may be on a system, it might not be accessible. It might be spread out across siloed systems, be out of chronological order, inappropriately titled, and most likely be buried amongst multiple sets of unstructured data, where it is poorly sorted.

These gaps in finding data often mean that multiple parties (often the patient’s GP) will have to be contacted, not only potentially increasing and duplicating data storage, but also leading to further delays in treatment. These difficulties are particularly problematic in out of hours and  in emergency situations, and at any point where there is a transfer of care from one service to another.

Completing the mental health record

As mental health practitioners, we know we need to reduce clinical risk and improve the quality of patient care by streamlining our access to a full patient history, leading to time and cost efficiencies.

You might argue that an electronic patient record (EPR) will solve this. But although the most up-to-date EPR systems attempt to provide effective access to a full patient history, there are still difficulties in how data from multiple sources is stored and organised to be readily available to the EPR and clinicians.

The structured and proprietary nature of EPRs does not always align with clinical data gathering workflows, which is further complicated if there is a requirement to search through legacy data. In order to overcome these issues, there is a need for a single source or repository of all data pertaining to a patient – an independent clinical archive (ICA).

An ICA can aggregate historic and referenceable patient information, regardless of the data format, from a wide range of applications — from across disparate hospital departments or external healthcare organisations — making that data available to clinicians to inform more accurate decision-making and reduce clinical risk.

As the Wachter Review had stated, “the goal of digitisation of health systems is to promote healthcare’s triple aim: better health, better healthcare, and lower cost;” and in order to achieve this, we must arm practitioners with the proper tools to make quick, accurate diagnosis and treatment at the point of care.

Gabriel MaDr Gabriel Ma is clinical lead at BridgeHead Software and a former NHS psychiatry doctor.

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